Research & Results
Statins may be effective treatment for patients with ulcerative colitis
September 15, 2021. "People with ulcerative colitis who are also taking statins have about a 50% decreased risk of colectomies and hospitalization, according to a Stanford Medicine study." DBDS faculty researchers Purvesh Khatri is featured and Nigam Shah is mentioned.
Data-powered consult service shown to help doctors diagnose illness, guide treatments
September 15, 2021. "Stanford Medicine researchers created a new type of medical consult that harnesses millions of electronic health records to bring new insights to patient care." DBDS faculty researchers Nigam Shah is featured and Trevor Hastie is mentioned.
RNA splicing programs define tissue compartments and cell types at single cell resolution
September 13, 2021. Members of the Salzman Lab, including co-first authors Julia Olivieri (pictured here) and Roozbeh Dehghannasiri, Peter Wang, Julia Salzman, and colleagues explore the extent splicing is regulated at single-cell resolution in this new eLife publication.
Why radiologists should consider earlier follow-up imaging for many Lung-RADS cases
August 19, 2021. New evidence from the Stanford Plevritis Lab suggests providers may want to consider ordering follow-up CTs for probably benign nodules earlier than currently suggested. Doing so reduced mortality rates, among other health benefits.
New npj Digital Medicine publication by Akshay Chaudhari, demonstrating prospective use of deep learning to improve the quality of 4x low-dose PET imaging studies
August 18, 2021. Through an external multi-institutional, multi-vendor, and multi-reader evaluation, DBDS Courtesy faculty member Akshay Chaudhari et al. showed that deep learning (DL) can help maintain image quality of fourfold low-dose PET scans without compromising qualitative and quantitative outcomes for patients. This study showed the generalizability of the DL system across different PET scanners, acquisition protocols, and patient habitus.
New Genetic Tech Can Fight Inherited Heart Disease – And Families Can, Too
August 18, 2021. DBDS researcher Dr. Euan Ashley is interviewed in this American Heart Association (ASA) news release, saying "In this new world, we're able to actually sequence genes & give people definitive answers. And that's powerful – first of all, just because having an answer is a powerful thing. But even much more important, it's actionable."
New preprint on the analysis of longitudinal randomized trial data by Adjunct Prof. Alejandro Schuler
August 17, 2021. Adjunct Professor Alejandro Schuler (pictured here) released his preprint “Mixed models for repeated measures should include time-by-covariate interactions to assure power gains and robustness against dropout bias relative to complete-case ANCOVA" on arXiv. The work shows that a popular method of analyzing data from longitudinal randomized trials can actually result in worse confidence and more bias than a naive method unless the effects of baseline covariates are allowed to vary in time.
New Plevritis Lab research on the ENGAGE framework for risk-based lung cancer screening published in Cancer
August 12, 2021. A paper from DBDS Plevritis Lab researchers, Iakovos (Jacob) Toumazis (pictured here) and Sylvia Plevritis, and colleagues describing the findings of applying the ENGAGE framework – a dynamic risk-based lung cancer screening framework delivering personalized policies – has been published in Cancer. Accompanying this publication, an editorial exploring how the ENGAGE framework will continue to enlighten and shape the ongoing conversation around the delivery of personalized lung cancer screening has also been published.
New BMJ publication investigating the real-world impact of a healthcare ML/AI system by BMI PhD candidate Ben Marafino
August 11, 2021. A new article in BMJ authored by BMI PhD candidate Ben Marafino, together with Professor Mike Baiocchi (Epidemiology and Population Health) and BMI PhD alum (now adjunct professor of DBDS) Alejandro Schuler, is among the first to characterize the real-world impact of a ML/AI system at scale in healthcare. The study, which was carried out in collaboration with Kaiser Permanente, examined data on nearly 2 million patients before and after the implementation of a predictive-algorithm driven intervention to reduce readmission, and employed a novel hybrid regression discontinuity/difference-in-differences design approach. Co-authors include Gabriel Escobar, Vincent Liu, and Colleen Plimier, all of Kaiser Permanente.
Tackling COVID-19 Among Prison Populations in California and Beyond
August 9, 2021. The Stanford Health Policy prison project team, which includes DBDS/BMI researcher Elizabeth T. Chin, is out with two more studies to help prisons prevent and reduce the spread of the coronavirus. Read the Stanford Health Policy News release.
Work on clinical trial design accounting for efficient estimation published in IJB by Adjunct Prof. Alejandro Schuler
August 6, 2021. Adjunct Professor Alejandro Schuler's paper, “Designing efficient randomized trials: power and sample size calculation when using semiparametric efficient estimators," was published in the International Journal of Biostatistics. The work proposes a method that leverages efficient estimators to prospectively design smaller and faster randomized trials while attaining the same power that would otherwise only be possible with a larger sample size.
Ten Rules for Conducting Retrospective Pharmacoepidemiological Analyses: Example COVID-19 Study
July 28, 2021. DBDS Instructor Suzanne Tamang (pictured here) and colleagues summarize "10 rules that serve as an end-to-end introduction to retrospective pharmacoepidemiological analyses of observational health care data using a running example of a hypothetical COVID-19 study" in this Frontiers in Pharmacology article.
Rooting Out Anti-Muslim Bias in Popular Language Model GPT-3
July 22, 2021. This HAI Blog post features new research by Zou group researchers Abubakar Abid and James Zou, and colleague, that argues that “severe” anti-Muslin bias must be addressed before language models become ingrained in real-world tasks.
Stanford researchers develop tool to drastically speed up the study of enzymes
July 22, 2021. A new Science research article by DBDS faculty researcher Chiara Sabatti (pictured here) and Stanford colleagues is featured in this EurekAlert release, about a new tool that will help researchers examine the enzymes that power remarkable transformations in animals and plants.
Genetics could explain why some people get severe COVID-19
July 21, 2021. One of the greatest mysteries of the COVID-19 pandemic is why some people fall severely ill while others do not. Now, after compiling data from around the world, DBDS researchers Manuel Rivas, Carlos D. Bustamante, Euan Ashley, and collaborators have determined the answer seems to lie, in part, in genetics.
Virus or Bacterium? Rapid Test Pinpoints Infection’s Cause
July 20, 2021. DBDS researcher Purvesh Khatri is quoted in this Scientific American story about a generation of new tests that could lessen overuse of antibiotics.
Study shows why second dose of COVID-19 vaccine shouldn’t be skipped
July 17, 2021. Scientists, including DBDS faculty researcher Purvesh Khatri, scrutinized Pfizer vaccine recipients’ blood samples to learn exactly what effects the vaccine exerts on the body’s immune system.
Immune system “clock” predicts illness and mortality
July 12, 2021. Scientists at Stanford and the Buck Institute, including DBDS faculty researchers Rob Tibshirani and Trevor Hastie, have found a way to predict an individual’s immunological decline as well as the likelihood of incurring age-associated diseases and becoming frail.
Systems vaccinology of the BNT162b2 mRNA vaccine in humans
July 12, 2021. This paper on systems vaccinology of the Pfizer-BioNTech vaccine was published today in Nature, and authored by DBDS faculty member Purvesh Khatri and colleagues.
An evidence-based framework for evaluating pharmacogenomics knowledge for personalized medicine
July 3, 2021. The Klein lab has developed an evidence-based framework for evaluating pharmacogenomics knowledge for personalized medicine and published it in Clinical Pharmacology and Therapeutics. This framework is used for annotation and integration of pharmacogenomics literature for the PharmGKB project and provides a scalable, standardized approach to assigning a ‘level of evidence’ to associations between genetic variants and drug response.
Stanford researcher’s cryptography can preserve genetic privacy in criminal DNA profiling
June 30, 2021. "Crime scene DNA analysis can help identify perpetrators, but current methods may divulge the genetic information of innocent people. Cryptography can protect genetic privacy without hampering law enforcement, Stanford researchers say." DBDS faculty member Gill Bejerano is featured in this story.
Disparity in the quality of COVID-19 data reporting across India
June 28, 2021. DBDS faculty member James Zou and colleagues released the results of their latest research in this BMC Public Health research article, which presents a comprehensive assessment of the quality of COVID-19 data reporting done by the Indian state governments between 19 May and 1 June, 2020.
Google Launches a New Medical App—Outside the US
June 23, 2021. DBDS Postdoctoral Research Fellow Roxana Daneshjou is interviewed in this WIRED story about Google's new dermatology AI app, which won approval for use in the EU but not with the FDA. While exploring the question of whether AI systems can provide equally accurate diagnoses as US board-certified dermatologists, the story also references a recent Nature Medicine study by members of the Zou group, including Eric Wu, Kevin Wu, Daneshjou, James Zou, and colleagues.
Stanford Scholars Build AI-Based Tool To Scrutinize COVID Research
June 21, 2021. Postdoctoral Fellow Jake Lever and Stanford Bioengineering and DBDS faculty member, Russ Altman built CoronaCentral, a web-based dashboard of coronavirus-related articles that can help people unearth trends and reveal new avenues for research. This HAI News story examines how it can help scholars and policymakers understand COVID's impact.
Climate change linked to longer allergy season in Bay Area, Stanford study finds
June 17, 2021. Biomedical Data Science researcher Bibek Paudel is featured in this Stanford Medicine News release about a Stanford study that has found air levels of pollen and mold spores in the San Francisco Bay Area are elevated for about two more months per year than in past decades, and higher temperatures are to blame.
Tina Hernandez-Boussard: How data improves the quality of health care
June 14, 2021. A specialist in bioinformatics, DBDS faculty member Tina Hernandez-Boussard, explains that the tools of data science are delivering insights into health care outcomes and improving care as never before.
Patent citations could signal which biomedical papers lead to a real-world impact
June 10, 2021. By tracking which scientific papers are cited by patents, researchers can quantify which studies contribute to real-world applications. This study, published in Nature Biotechnology and authored by Stanford researchers, including DBDS faculty member James Zou (pictured here), uses millions of patent citations to study how biomedical innovations translate to practice.
AI for Predicting COVID-19 Prognosis
June 10, 2021. A Stanford team, including researchers in Olivier Gevaert's lab, used quantitative image analysis and data fusion to predict COVID severity in patients. The approach could prove useful beyond coronavirus.
Can AI Create Faster, More Reliable MRI Scans?
May 17, 2021. A new publication from Akshay Chaudhari and colleagues accepted as a long presentation in the upcoming International Conference on Machine Learning characterizes the robustness of supervised and unsupervised image reconstruction methods for rapid magnetic resonance imaging, leading the path towards reliable clinical translation. An extended description can be found in the following report by Stanford HAI.
Bio Eats World: The Trials of Clinical Trials
May 18, 2021"Host Lauren Richardson talks to James Zou, Assistant Professor of Biomedical Data Science at Stanford University, and a16z general partner Vineeta Agarwala, physician and expert on real world data in healthcare, about new research from the Zou lab that uses AI-powered simulations of clinical trials and real world patient data to understand how different designs influence trial outcomes. In particular, looking for designs that can make trials more inclusive, which is key for getting patients access to potentially life-saving care and for running trials efficiently. The conversation covers the inherited rules and assumptions governing which patients can participate in trials, how Dr. Zou, lead author Ruishan Liu, and colleagues combined real world data and computer simulations to challenge these assumptions via a data-driven approach, and how this can inform smarter trial design.
The article at the center of this episode is: “Evaluating eligibility criteria of oncology trials using real-world data and AI” by Ruishan Liu, Shemra Rizzo, Samuel Whipple, Navdeep Pal, Arturo Lopez Pineda, Michael Lu, Brandon Arnieri, Ying Lu, William Capra, Ryan Copping & James Zou, published in Nature."
Smartwatch data can predict blood test results, study reports
May 24, 2021. Stanford researchers, including DBDS faculty member Trevor Hastie, found that data from smartwatches can flag early signs of some health conditions and predict the results of simple blood tests.
Agile NLP for Clinical Text: COVID-19 and Beyond
June 1, 2021. “In early 2020, just as the SARS-CoV-2 virus was arriving in the United States, a team of Stanford researchers wondered if the natural language processing (NLP) framework they were developing might be nimble enough to help triage COVID-19 patients who visited the Stanford Hospital emergency room. 'There’s lots of useful information in doctors’ notes and unstructured textual medical records, and we wanted a fast way to get it out, given the COVID-19 pandemic situation,' says Nigam Shah, professor of medicine (biomedical informatics) and of biomedical data science at Stanford University and an affiliated faculty member of the Stanford Institute for Human-Centered Artificial Intelligence.
Machine learning is booming in medicine. It’s also facing a credibility crisis
June 2, 2021. “We would like the AI to work responsibly and reliably for different patients in different hospitals,” said James Zou, a professor of biomedical data science at Stanford and co-author of a recent paper that highlighted the lack of prospective studies, or studies that examine future outcomes, conducted on even higher-risk AI products cleared by the FDA. “So it’s especially important to be able to evaluate and test the algorithm across these diverse kinds of data.”
Cognoa Receives FDA Marketing Authorization for First-of-its-kind Autism Diagnosis Aid
June 2, 2021. We are thrilled to announce the machine learning tool that DBDS faculty member Dennis Wall (pictured here) designed for autism diagnosis just received clearance by the FDA. "Cognoa’s AI-driven device is the first FDA-authorized diagnosis aid designed to help primary care physicians diagnose autism in young children with the goal of shortening time-to-diagnosis and enabling initiation of earlier interventions." This represents a big step to translation for AI-medical devices, and a great case study for translational bioinformatics.
A Tale of Medical Mysteries Unraveled by Genetic Detectives
June 7, 2021. Euan Ashley is interviewed by Bob Harrington, MD, in this Medscape Cardiology video.
Debiasing artificial intelligence: Stanford researchers call for efforts to ensure that AI technologies do not exacerbate health care disparities
May 14, 2021. DBDS faculty member James Zou is featured in this Stanford News release. "Medical devices employing AI stand to benefit everyone in society, but if left unchecked, the technologies could unintentionally perpetuate sex, gender and race biases." Access the release
June 8, 2021 Update: Stanford Scope Blog released a condensed version of this story. Access the blog post
Stanford postdoc enters her youngsters in vaccine COVID trial
April 30, 2021. "Anxious to protect her children, Stanford immunology researcher Zina Good has enrolled her two young children Pfizer’s COVID-19 vaccine clinical trial for kids." Dr. Good is also a postdoctoral research fellow in Sylvia Plevritis's DBDS Lab.
Reconstructing co-dependent cellular crosstalk in lung adenocarcinoma using REMI
May 3, 2021. DBDS researchers Alice Yu (pictured here), Yuanyuan Li, Christine Yeh, Aaron Chiou, Sylvia Plevritis, & Stanford colleagues have released a new preprint, "Reconstructing co-dependent cellular crosstalk in lung adenocarcinoma using REMI."
Encouraging Sign: Many California Prisoners Willing To Be Vaccinated
May 12, 2021. "Two-thirds of the nearly 100,000 incarcerated residents in California's 35 prisons were offered COVID-19 vaccines and 66.5% of those accepted at least one dose, according to a new Stanford study — although uptake varied across different groups." Elizabeth T. Chin, the lead author of the study and a PhD candidate in biomedical data science, is featured in this story.
Unequal Treatment: How considering race sabotages care — and why change is imperative
May 15, 2021. DBDS faculty member Carlos D. Bustamante is featured in this Stanford Medicine Magazine story, which explores his recent efforts to understand how genetics influences immunity and response to COVID-19. "Variation in the genes linked to COVID-19 are not specific to a racial or ethnic groups, but rather shared across groups, said Bustamante." This story appears in the magazine's May 2021 "Closing the Gap: Addressing Racial Inequity in Medicine" special issue.
How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals
April 5, 2021. Electrical Engineering PhD student Eric Wu, BMI PhD student, Kevin Wu, and members of the Zou Group, including Roxana Daneshjou, David Ouyang, and James Zou, and Stanford colleagues published a research paper in Nature Medicine.
BABEL enables cross-modality translation between multiomic profiles at single-cell resolution
April 13, 2021. DBDS community members Kevin Wu and James Zou, with Stanford colleagues Kathryn Yost and Howard Chang, introduce a deep learning algorithm that flexibly translates between chromatin, RNA, and protein profiles in single cells in this PNAS publication.
New AI model could make clinical trials more inclusive
April 21, 2021. Trial Pathfinder, a new artificial intelligence model developed by Stanford researchers, could help researchers broaden eligibility criteria for clinical trials without compromising participants’ safety. The technology was developed by James Zou, assistant professor of biomedical data science, and Ruishan Liu, a PhD student in Zou’s lab.
AI-based analysis of CT images for rapid triage of COVID-19 patients
April 22, 2021. Great work by students & trainees in the Gevaert lab developing an AI-based approach combining clinical, lab data and CT imaging to triage COVID19 patients. Congratulations to Qinmei "May" Xu, Xianghao(Sam) Zhan, Yiheng "Terry" Li and Peiyi "Penny" Xie!
Mouse aging cell atlas analysis reveals global and cell type-specific aging signatures
April 13, 2021. "Aging is associated with complex molecular and cellular processes that are poorly understood. [In this eLife Sciences publication, members of the Zou Group and colleagues] leveraged the Tabula Muris Senis single-cell RNA-seq data set to systematically characterize gene expression changes during aging across diverse cell types in the mouse."
Tissue specificity-aware TWAS (TSA-TWAS) framework identifies novel associations with metabolic, immunologic, and virologic traits in HIV-positive adults
April 27, 2021. DBDS Postdoctoral Fellow from the Klein Group, Binglan Li, is first author on this early release of a new PLOS study.
The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset
May 3, 2021. DBDS faculty member Akshay Chaudhari and colleagues aimed "to organize a multi-institute knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression" in this new RSNA publication.
How artificial intelligence could make clinical trials smarter
April 7, 2021. This STAT Health Tech story features DBDS faculty member James Zou and his machine learning/AI research.
Should AI Models Be Explainable? That depends.
March 16, 2021. DBDS faculty member Nigam Shah advocates for clarity about the different types of interpretability and the contexts in which it is useful in this HAI Blog post.
Study Outlines Testing Strategies for Safer Air Travel During the Pandemic
March 23, 2021. "Almost 90 percent of infectious travelers could be detected with rapid SARS-CoV-2 tests at the airport, and most imported infections could be prevented with a combination of pre-travel testing and a five-day post-travel quarantine that would only lift with a negative test result, according to a computer simulation by UC San Francisco researchers." BMI PhD Students Elizabeth Chin and Benjamin Huynh are listed as co-authors, along with Stanford and UCSF colleagues.
Stanford’s look back on one year of the pandemic
March 18, 2021. DBDS faculty member Russ Altman is featured in this video, produced by Stanford Today. "March 19 marks the one-year anniversary of California’s stay-at-home order. Despite a year apart, the Stanford community has contributed in meaningful ways by shifting research to focus on COVID-19, finding creative ways to teach remotely, connecting with the arts from home and helping other communities."
Stories of a Disease Detective
March 15, 2021. Geneticist, and DBDS faculty member, Euan Ashley applies his love of Sherlock Holmes mysteries to a book about helping patients with undiagnosed diseases find answers.
Truth and Reconciliation of Racial and Ethnic Health Disparities: A Case Study of COVID-19
March 15, 2021. The American Journal of Bioethics has e-published DBDS Postdoctoral Research Fellow, Alice Popejoy's new single-authored paper, a case study of COVID-19. It will also be published in the journals' March print edition.
A Story One Year in the Telling: the Stanford COVID Modeling Project
March 11, 2021. "The Stanford-CIDE Coronavirus Simulation Model was established in the frightening days when the world was realizing a deadly virus in China would become a pandemic. A look at its accomplishments and projects one year later." Featured projects include the the Prisons and Jails Project, for which BMI PhD Student Elizabeth Chin led the work to analyze data and create high resolution models of transmission and simulate the effects of prevention interventions, including vaccination.
Expanding evidence leads to new pharmacogenomics payer coverage
March 1, 2021. Teri Klein (pictured here) and colleagues released this Comment, with bearing on reimbursement for pharmacogenomics, in Genetics in Medicine.
Stanford Medicine launches in-house service for whole genome sequencing
February 11, 2021. DBDS faculty member Euan Ashley is featured in this Stanford Medicine News release about "a new Stanford Medicine service [that] analyzes patients’ entire genetic code for information that could reveal the roots of diseases. The service is based on whole genome sequencing, a test that maps all of an individual’s DNA."
Silicon Valley Prescribes ‘Big Data' to Combat COVID-19
February 10, 2021. By analyzing health records of past COVID-19 patients at Stanford Hospital, DBDS faculty Nigam Shah (pictured here) and team discovered that, in many cases, patients released from ICU and sent home did just as well as those who remained in the ICU.
Survival Analysis on Rare Events Using Group-Regularized Multi-Response Cox Regression
February 9, 2021. First author Ruilin Li (Rivas Lab member, pictured here) and DBDS community members Yosuke Tanigawa, Johanne Justesen, Trevor Hastie, Robert Tibshirani, and Manuel Rivas contributed the results of their new survival analysis to Bioinformatics.
“Even if you can do it, should you?” Researchers talk combating bias in artificial intelligence
February 3, 2021. "As artificial intelligence becomes increasingly common in several areas of public life — from policing to hiring to healthcare — AI researchers Timnit Gebru, Michael Hind, James Zou (DBDS) and Hong Qu came together to criticize Silicon Valley’s lack of transparency and advocate for greater diversity and inclusion in decision making. The event, titled 'Race, Tech & Civil Society: Tools for Combating Bias in Datasets and Models,' was sponsored by the Stanford Center on Philanthropy and Civil Society, the Center for Comparative Studies in Race and Ethnicity and the Stanford Institute for Human-Centered Artificial Intelligence (HAI)."
How Does Mixup Help Robustness and Generalization?
January 22, 2021. Members of the Zou Group, including PhD Student Amirata Ghorbani and DBDS faculty member James Zou, released the results of their latest preprint to arXiv.org. The study shows how a strategy called Mix-up improves the reliability of machine learning models. It has been accepted as a spotlight paper at the International Conference on Learning Representations (ICLR 2021). Stanford HAI released a story about the new study on January 27, 2021.
Polygenic risk modeling with latent trait-related genetic components
January 14, 2021. Rivas Lab members Matthew Aguirre (pictured here), Yosuke Tanigawa, and Guhan Venkataraman, and DBDS faculty members Rob Tibshirani, Trevor Hastie, and Manuel Rivas, showcase the results of their recent research in this new European Journal of Human Genetics article.
Graphical analysis for phenome-wide causal discovery in genotyped population-scale biobanks
January 13, 2021. Postdoctoral Fellow, David Amar (Rivas/Ashley Labs), Manuel Rivas, and Stanford colleagues contributed the results of their new causal inference research to Nature Communications.
Genetics of 35 blood and urine biomarkers in the UK Biobank
January 18, 2021. Members of the Rivas Lab, including Yosuke Tanigawa (pictured here), Matthew Aguirre, Guhan Venkataraman, Junyang Qian, and Manuel Rivas, and DBDS community researchers David Amar, Anna Shcherbina, Rob Tibshirani and Trevor Hastie collaborated with Stanford and international colleagues to release the results of their UK Biobank research in Nature Genetics.
A meta-learning approach for genomic survival analysis
January 11, 2021. Members of the Geveart Lab and Stanford BMIR, including Yeping Qiu, Hong Zheng (pictured here), Heather Selby, and Olivier Gevaert, and colleague released the results of their recent research in a December 2020 Nature Communications study.
Predicting premature birth in low-resource settings
January 12, 2021. A recent JAMA Network Open study by DBDS faculty member Nima Aghaeepour (pictured here) and Stanford colleagues, arguing that most preterm births can be predicted using blood and urine samples collected early in the pregnancy, is featured in this Stanford Scope Blog post.
Based on genes, nearly everyone is likely to have an atypical response to at least one drug
January 4, 2021. This Stanford Scope Blog post introduces a new study by DBDS faculty member Russ Altman and colleagues that suggests that when it comes to drug doses, "one size fits all" rarely applies.
Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study
January 1, 2021. DBDS Postdoctoral Scholar Riyika Yamashita (pictured here), DBDS faculty member Daniel Rubin and colleagues released a new study in The Lancet Oncology, investigating the potential of a deep learning-based system for automated microsatellite instability prediction.
The Newest Weapon Against Covid-19: AI That Speed-Reads Faxes
December 22, 2020. DBDS community members Adam Lavertu (pictured here) and Russ Altman are featured in this WIRED Magazine article about a new machine-learning program that can help local health departments track COVID-19 cases.
New Interview with Ying Lu and Tze Lai in Statistical Science
December 21, 2020. Statistical Science has published a conversation between DBDS faculty members Ying Lu (and colleagues) and Tze Lai (pictured here). This conversation began in June 2015 in the Department of Statistics at Columbia University during Lai’s visit to his alma mater where he celebrated his seventieth birthday. It continued in the subsequent years at Columbia and Stanford.
Multiomics Characterization of Preterm Birth in Low- and Middle-Income Countries
December 18, 2020. DBDS faculty member Nima Aghaeepour (pictured here) and colleagues released the results of their recent research that suggests that most preterm births can be predicted using blood and urine samples collected early in the pregnancy, providing opportunities for interventions, in this JAMA Network Open publication.
An Interview with Bradley Efron
November 22, 2020. DBDS Senior Research Scientist and Director of the Data Coordinating Center, Balasubramanian Narasimhan (pictured here) interviewed DBDS faculty member, Bradley Efron for this new publication in the International Statistical Review.
DBDS featured in new Center for Digital Health Landscape Report
November 17, 2020. Stanford's Center for Digital Health (CDH) is excited to announce the release of the Stanford Center for Digital Health Landscape Report, which features DBDS programs, projects and courses, and work by DBDS faculty researchers Euan Ashley, Daniel Rubin, and Russ Altman. A central goal of this report is to highlight the culture of collaboration that is found throughout the broader Stanford community.
The SZS is an efficient statistical method to identify regulated splicing events in droplet-based RNA sequencing
November 11, 2020. Members of Stanford's Salzman Lab, including ICME PhD Student Julia Eve Olivieri (pictured here), Postdoctoral Fellow in Biochemistry Roozbeh Dehghannasiri, and DBDS faculty member Julia Salzman, released their new bioRxiv preprint. This study introduces a novel, robust, and computationally efficient statistical method, the Splicing Z Score (SZS), to detect differential alternative splicing in single cell RNA-Seq technologies including 10x Chromium.
Distance metrics for ranked evolutionary trees
November 2, 2020. DBDS faculty member Julia Palacios (pictured here) and Stanford Biology colleagues Jaehee Kim and Noah Rosenberg published the results of their most recent research in PNAS.
When Algorithms Compete, Who Wins?
October 27, 2020. DBDS faculty member James Zou (pictured here) is featured in this HAI Blog post about how "Over time, prediction algorithms become specialized for an increasingly narrow slice of the population, and the average quality of their predictions declines."
Patient-Specific Induced Pluripotent Stem Cells Implicate Intrinsic Impaired Contractility in Hypoplastic Left Heart Syndrome
October 26, 2020. BMI Graduate Student, Elizabeth Chin (pictured here), co-first authored a new paper, which was published in Circulation. Co-authors include DBDS faculty member Euan Ashley.
A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank
October 26, 2020. DBDS community members, Yosuke Tanigawa (pictured here), Matthew Aguirre, Rob Tibshirani, Manuel Rivas, and Trevor Hastie, and Stanford and other colleagues including first-author Junyang Qian, published their newest research in PLOS Genetics.
Frequency of Routine Testing for Coronavirus Disease 2019 (COVID-19) in High-risk Healthcare Environments to Reduce Outbreaks
October 26, 2020. BMI Graduate Students Elizabeth Chin and Benjamin Huynh (pictured here) released the results of their latest research to Clinical Infectious Diseases. They are co-first authors on this paper, and co-authors include Stanford MD-PhD alum, Nathan Lo.
Discovering our way out: A sampler of COVID-19 research
October 15, 2020. Check out this Stanford Medicine Magazine story featuring COVID-19 research by DBDS faculty Euan Ashley, Carlos D. Bustamante, and Manuel Rivas. So much amazing work being done in DBDS!
All together now: Stanford Medicine takes aim at COVID-19
October 15, 2020. Nigam Shah is featured in this new Stanford Medicine Magazine story about researchers' early responses to the COVID-19 pandemic.
Three new papers from the Zou Group now available
October 12, 2020. Three new papers from the DBDS Zou Group were recently accepted by NeurIPS, which is the main machine learning conference. These papers introduce FrugalML (improving commercial ML efficiency), Neuron Shapley (for identifying important artificial neurons in the network) and MOPO (for improving reinforcement learning on observational data).
Use the links above to access these papers
Fast Lasso method for large-scale and ultrahigh-dimensional Cox model with applications to UK Biobank
October 5, 2020. ICME PhD Student Ruilin Li (pictured here), colleague Christopher Chiang (GRAIL), and DBDS community members Johanne Justesen, Yosuke Tanigawa, Junyang Qiang, Trevor Hastie, Manuel Rivas, and Rob Tibshirani released the results of their recent research in Biostatistics. They developed a scalable and highly efficient algorithm to fit a Cox proportional hazard model by maximizing the L1L1-regularized (Lasso) partial likelihood function, based on the Batch Screening Iterative Lasso (BASIL) method.
ALICE: Active Learning with Contrastive Natural Language Explanations
September 28, 2020. DBDS faculty member James Zou and colleagues shared a new paper with Cornell University's arXiv on how to use natural language explanations to speed up training of machine learning. The paper was just accepted for publication in EMNLP Conference.
New Future of Everything Podcast with host Russ Altman
September 21, 2020. Host and DBDS professor Russ Altman explores how technology, science and medicine are shaping our lives in the "Future of Everything" podcast. A new episode, entitled "Can democracy survive in a digital world?," features Marietje Schaake, HAI International Policy Fellow.
Transcriptomic signatures across human tissues identify functional rare genetic variation
September 11, 2020. BMI Graduate Student Nicole Ferraro (pictured left) and Stanford and other colleagues including Stephen Montgomery (Genetics) had a paper published in Science Magazine, as part of set of GTEx papers.
AI researchers explore solutions for real-life health challenges
September 4, 2020. New AI research by DBDS faculty member Dennis Wall (pictured left) and Stanford colleagues is featured in this Stanford Scope Blog post.
Impact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx
September 11, 2020. A second study written by DBDS community members as part of the GTEx paper bundle in Science Magazine was also released. Authors included BMI Graduate Students Michael Gloudemans (pictured left) and Margaret Antonio, and Bustamante Lab alumni Alicia Martin and Shaila Mustaroff.
Sex-specific genetic effects across biomarkers
September 1, 2020. DBDS Community members Emily Flynn (pictured left), Yosuke Tanigawa, Russ Altman, and Manuel Rivas and Stanford colleagues released the results of their latest research in the European Journal of Human Genetics.
5 Questions: Alice Popejoy on race, ethnicity and ancestry in science
August 21, 2020. Alice Popejoy, a DBDS postdoctoral scholar who studies biomedical data sciences, speaks to the role — and pitfalls — of race, ethnicity and ancestry in research.
The Surprising Advantages of Virtual Conferences
August 21, 2020. In this Scientific American story, Russ Altman reflects on his experience organizing the COVID-19 and AI Virtual Conference in April.
Serena Yeung named recipient of new CZI award
August 19, 2020. The Chan Zuckerberg Initiative (CZI) released a press release announcing that DBDS faculty member Serena Yeung and Wah Chiu (Stanford Bioengineering) are co-recipients of the CZI Neurodegeneration Challenge Network Collaborative Pairs award.
Artificial intelligence recognizes deteriorating photoreceptors
August 13, 2020. A recent JAMA Ophthalmalogy paper authored by DBDS faculty member Daniel Rubin, postdoctoral fellow Maximilian Pfau and colleagues was featured in a EurekAlert! news release and also on Dr. Rubin's American Institute for Medical and Biological Engineering (AIMBE) College of Fellows Class of 2018 profile page.
A data scientist and former student researcher shares how the Broad Institute helped guide his career
July 28, 2020. DBDS faculty member, Manuel Rivas (pictured to the left) was featured in a Broad Institute News story.
New Op-Ed by Rob Tibshirani published by the New York Times
July 20, 2020. DBDS Faculty Robert Tibshirani and colleague Michael Eisen (UC Berkeley) contributed an Op-Ed, entitled "How to Identify Flawed Research Before It Becomes Dangerous," to the New York Times. They argue, "Scientists and journalists need to establish a service to review research that’s publicized before it is peer reviewed."
VoPo leverages cellular heterogeneity for predictive modeling of single-cell data
July 27, 2020. Members of the Aghaeepour Laboratory--including Natalie Stanley, Ramin Fallahzadeh, Martin Becker, Thanaphong Phongpreecha, Huda Nassar, Sajjad Ghaemi, Anthony Culos, Alan L. Chang, Maria Xenochristou, Camilo Espinosa, and Nima Aghaeepour (pictured left)--contributed their most recent research to Nature Communications.
RNA-GPS Predicts SARS-CoV-2 RNA Residency to Host Mitochondria and Nucleolus
June 20, 2020. DBDS members of the Zou Group, including Kevin E. Wu and James Zou, in collaboration with Stanford colleagues, released the results of their recent research in Cell.
Manisha Desai featured in Women in Data Science (WiDS) Podcast
July 17, 2020. On this episode of the WiDS Podcast, DBDS faculty member, Manisha Desai, shares some insights about the challenges and progress of current COVID-19 clinical trials.
Projected geographic disparities in healthcare worker absenteeism from COVID-19 school closures and the economic feasibility of child care subsidies: a simulation study
July 15, 2020. BMI PhD candidates Elizabeth Chin (pictured left) and Benjamin Huynh, as well as DBDS Prof Trevor Hastie, have a new publication in BMC Medicine, entitled "Projected geographic disparities in healthcare worker absenteeism from COVID-19 school closures and the economic feasibility of child care subsidies: a simulation study."
Variation in Health Care Prices Across Public and Private Payers
July 14, 2020. DBDS Instructor Suzanne Tamang and colleagues studied "a unique all-payer data set spanning 38 states to examine the differences in inpatient reimbursement rates paid by traditional Medicare, Medicare Advantage, Medicaid, and private (under-65) insurers," and released their results to The National Bureau of Economic Research.
Native American gene flow into Polynesia predating Easter Island settlement
July 8, 2020. Members of the Bustamante and Rivas Labs, including first author Alex Ioannidis (pictured left), contributed their recent research to Nature. It has been featured by a number of major publications: links for these stories are provided below.
Integrating spatial gene expression and breast tumour morphology via deep
June 22, 2020. Members of the DBDS Zou Group, including Abubakar Abid and James Zou, released the results of their recent research in Nature Biomedical Engineering.
FasTag: Automatic text classification of unstructured medical narratives
June 22, 2020. Members of the Rivas Lab, including Guhan Vankataraman (pictured left) and Manuel Rivas, and Carlos Bustamante released the findings of their recent research in PLOS One.
Clinical Genetics Lacks Standard Definitions and Protocols for the Collection and Use of Diversity Measures
June 5, 2020. Members of the DBDS Community, including first author Alice Popejoy, Carlos D. Bustamante, James Zou, and the Clinical Genome Resource (ClinGen) Ancestry and Diversity Working Group contributed a new study to AJHG.
CS 472 students tackle COVID-19
June 2, 2020. "CS 472: 'Data Science and AI for COVID-19” is featured, along with DBDS Faculty instructor James Zou, in this Stanford Daily story. This course "allows undergraduate students, graduate students and people around the world to unite in the fight against the novel coronavirus."
A Machine Learning Approach to Identifying Changes in Suicidal Language
June 2, 2020. DBDS Instructor Suzanne Tamang (pictured left) and colleagues published their recent research in Suicide and Life Threatening Behavior.
New publication from the Wall Lab on Common Microdeletions in SARS-CoV-2 Sequences
May 16, 2020. Stanford Wall Lab members Brianna Sierra Chrisman, Kelley Paskov, Nate Stockham, Jae-Yoon Jung, Maya Varma, Peter Washington, and Dennis P. Wall released the results of their recent COVID-19 research on virological.org.
A human lung tumor microenvironment interactome identifies clinically relevant cell-type cross-talk
May 7, 2020. Members of DBDS's Gentles and Plevritis Labs, including PIs Andrew Gentles and Sylvia Plevritis, released the results of their recent research in Genome Biology.
Stanford researchers working on wearables for early detection of infectious diseases
May 5, 2020. DBDS faculty member Russ Altman is featured in this Stanford Daily story about how "Stanford researchers are working with Fitbit and Scripps Research Institute to develop wearables that can detect infectious diseases such as COVID-19 and help contain their spread."
Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma
May 5, 2020. This study from members of the Rivas Lab, including first author Yosuke Tanigawa (pictured left), has been published by PLOS Genetics.
June 8, 2020 Update: This paper was highlighted as Editors' Choice in Science.
Knight-Hennessy Scholars program announces new cohort
May 4, 2020. We are thrilled to announce that two of our incoming BMI students, Misha Baitemirova and Eric Sun, have been awarded Knight-Hennessy Scholarships. Including Juan Manuel Zambrano, a returning BMI PhD scholar who was awarded a KH Fellowship two years ago, we will have three KH scholars in the BMI program!
Specific splice junction detection in single cells with SICILIAN
April 15, 2020. DBDS faculty member Julia Salzman and members of her lab, postdoc Roozbeh Dehghannasiri and graduate student Julia Olivieri (pictured left), released their preprint on bioxRiv!
Why Do Young, Healthy People Die from COVID-19?
April 14, 2020. DBDS faculty member Manuel Rivas contributes to this Proto Magazine story about why young, healthy people die from COVID-19. His genetics work focuses on outliers at the other extreme: people who are highly resistant to the virus despite repeated exposure.
Assessing Digital Phenotyping to Enhance Genetic Studies of Human Diseases
April 9, 2020. DBDS community members and alumni, Christopher DeBoever (pictured left), Yosuke Tanigawa, Matthew Aguirre, Greg McInnes, Adam Lavertu, and Manuel Rivas, published the results from their recent research on digital phenotyping in AJHG.
Nigam Shah: A researcher turns to data to fight the COVID-19 virus
April 8, 2020. DBDS faculty member Nigam Shah joined Russ Altman for this Future of Everything podcast. Shah, "an expert in bioinformatics...describes how better information and modeling can help caregivers stay a step ahead of the new virus."
COVID-19 patients often infected with other respiratory viruses, preliminary study reports
March 29, 2020. DBDS faculty member Nigam Shah is featured in this Stanford Medicine News story about a preliminary analysis that finds that people infected with the virus that causes COVID-19 are often co-infected with other respiratory viruses.
How sick will the coronavirus make you? The answer may be in your genes
March 27, 2020. DBDS faculty member Manuel Rivas is quoted in this Science Magazine story, and his group's recent preprint is also mentioned.
COVID-19 Host Susceptibility Studies Ramp Up Internationally
March 26, 2020. DBDS faculty members Carlos Bustamante (pictured left) and Manuel Rivas are featured in this GenomeWeb article about The COVID-19 Host Genetics Initiative.
Video-based AI for beat-to-beat assessment of cardiac function
March 25, 2020. DBDS faculty member James Zou and colleagues published their most recent research in Nature. This paper presents the first AI system to assess cardiac function. It also makes publicly available one of the largest medical video datasets as a community resource. Stanford Medicine News also published a feature story about this groundbreaking research.
Initial Review and Analysis of COVID-19 Host Genetics and Associated Phenotypes
March 24, 2020. DBDS community members Yosuke Tanigawa (pictured left) and Manuel Rivas wrote a pre-print related on COVID-19 that is now posted on preprints.org.
Healthcare worker absenteeism, child care costs, and COVID-19 school closures: a simulation analysis
March 23, 2020. DBDS community members, Elizabeth Chin (pictured left) and Benjamin Huynh (PhD students in Biomedical Informatics Training Program), and faculty member Trevor Hastie, with UCSF and Harvard Medical School collaborators, wrote this timely preprint, which is now available online.
Peripheral T cell expansion predicts tumour infiltration and clinical response
February 26, 2020. Alumnus of the Biomedical Informatics (BMI) Training Program and Principal Scientist of Bioinformatics and Computational Biology at Genentech, Thomas Wu released the results of his recent research in Nature.
The automatic construction of bootstrap confidence intervals
February 18, 2020. DBDS faculty member Bradley Efron's and, pictured left, Senior Research Scientist Balasubramanian Narasimhan's new paper on automatic construction of bootstrap confidence intervals was recently published in the Journal of Computational and Graphical Statistics.
When AI is watching patient care: Ethics to consider
February 10, 2020. DBDS faculty member Serena Yeung is featured in this Stanford Scope Blog post about her recent Viewpoint, published in JAMA.
Unprecedented exploration generates most comprehensive map of cancer genomes charted to date
February 5, 2020. DBDS faculty members Carlos D. Bustamante and Francisco De La Vega (pictured left) were major contributors to the final publications of a series of papers by the PanCancer Analysis of Whole Genomes (PCAWG) consortium, released this week in Nature publications. They were involved for a number of years in this initiative, mainly working on the analysis of the germline genome of the studys' cancer patients.
Ethical and Legal Aspects of Ambient Intelligence in Hospitals
January 24, 2020. DBDS faculty member Serena Yeung and colleagues published a Viewpoint about “Ethical and Legal Aspects of Ambient Intelligence in Hospitals” on JAMA Network.
Study sheds light on the genetics of hibernation
January 23, 2020. DBDS Communications Manager, Katie M. Kanagawa, worked closely with DBDS faculty member Carlos D. Bustamante and DBDS Bustamante Lab alum, Katharine Grabek, to develop this Stanford Scope blog post, summarizing their recent research on the genetics of hibernation and exploring the significance of their findings for squirrels and humankind.
Single-cell transcriptional diversity is a hallmark of developmental potential
January 23, 2020. DBDS faculty member Aaron Newman and colleagues released the findings from their recent research in Science Magazine. This research is also featured in a Stanford Medicine News story, entitled "A single number helps Stanford data scientists find most dangerous cancer cells."
Genetic variation drives seasonal onset of hibernation in the 13-lined ground squirrel
December 20, 2019. This research by DBDS Bustamante Lab alumni, including Katharine Grabek, Thomas Cooke, Kaitlyn Spees, Shirley Sutton, and DBDS faculty Carlos Bustamante, was published in Nature Communications Biology.
Lower BMI means lower diabetes risk, even among non-overweight people
December 10, 2019. Lower body mass index (BMI) is consistently associated with reduced type II diabetes risk, among people with varied family history, genetic risk factors and weight, according to a new study published this week in PLOS Medicine by DBDS faculty Manuel Rivas of Stanford University, and colleagues.
Mapping lung cancer epithelial-mesenchymal transition states and trajectories with single-cell resolution
December 6, 2019. This study, published in Nature Communications, features Stanford research from DBDS Chair Sylvia K. Plevritis, faculty member Robert Tibshirani, members of the Plevritis lab including Benedict Anchang and first-author Loukia Karacosta, and others.
Stanford researchers program cancer-fighting cells to resist exhaustion, attack solid tumors in mice
December 4, 2019. DBDS postdoctoral scholar in the Plevritis Lab, Zinaida Good, and colleagues were featured in a Stanford Medicine News Center story about how “CAR-T cells are remarkably effective against blood cancers, but their effect can be transient as the cells become exhausted. Stanford researchers found a way to keep the cells effective in mice with human tumors.” This story was featured in the 12/5 Stanford Report.
Stanford-led snapshot of artificial intelligence reveals challenges
November 26, 2019. DBDS Faculty member, Russ Altman, is featured in a Stanford News story about how a "A periodic review of the artificial intelligence industry revealed the potential pitfalls of outsourcing our problems for technology to solve rather than addressing the causes, and of allowing outdated predictive modeling to go unchecked." This story was also included in the December 3rd issue of the Stanford Report.
Through Apple Heart Study, Stanford Medicine researchers show wearable technology can help detect atrial fibrillation
November 13, 2019. DBDS Faculty member, Manisha Desai, is featured in this Stanford Medicine News Center story about a "Study [that] shows that Apple Watch app can identify heart rhythm irregularities, which can help catch atrial fibrillation." This story was also included in the November 27th issue of The Brief by Dean Lloyd Minor.
Sex and gender analysis improves science and engineering
November 6, 2019. DBDS Primary Faculty member, Dr. James Zou, and co-authors released results from their research on how to use sex and gender analysis to improve science and engineering in Nature.
Sex and gender analysis improves science, Stanford scholars say
November 6, 2019. Stanford News featured research conducted by co-author, and DBDS faculty member, James Zou on how "a gender and sex analysis in scientific research can open the door to discovery and innovation."
Smartphone app encourages physical activity, study finds
October 31, 2019. DBDS Secondary faculty member, Euan Ashley, was featured in Stanford Medicine News and the Dean's Brief. The story explores how, "Using a smartphone app, Stanford scientists and their colleagues conducted the first entirely digital randomized clinical trial to boost exercise among participants."
Genome-wide Association Studies in Ancestrally Diverse Populations: Opportunities, Methods, Pitfalls, and Recommendations
October 10, 2019. Check out this new Cell Primer on GWAS in diverse populations. Authors, including DBDS Postdoctoral Scholar Alice Popejoy and Bustamante Lab alum Alicia Martin, worked really hard to make sure that race, ethnicity, and ancestry were considered carefully from an interdisciplinary perspective.
Medical device safety in the real world: Tapping EHR data
October 7, 2019. This Stanford Medicine News feature story features DBDS Secondary Faculty member, Dr. Nigam Shah, and explores how "Researchers used artificial intelligence and de-identified data from electronic health records to identify the safest types of hip implants."
Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology
September 6, 2019. Check out this new study, published today in Nature Communications, featuring research and results from the DBDS Rivas Lab, colleagues, and collaborators!
5 Questions: Tina Hernandez-Boussard on using ‘real-world data’ to inform clinical care
September 3, 2019. "In an interview, computational biologist [and DBDS Secondary Faculty/BMI Faculty] Tina Hernandez-Boussard discusses analyzing the value of electronic health records as a source of information in the clinic."
Rubin Lab Names One of Best Four 2018 Papers on Clinical Decision Support by IMIA!
September 3, 2019. The Rubin Lab's paper,* on AI modeling to predict survival of metastatic cancer patients to enable personalized treatment decision making, was named one of the 4 best papers (among 1,148 papers reviewed) published in 2018 on Clinical Decision Support by the International Medical Informatics Association (IMIA)!
16 new gene-based abnormalities found to increase risk for autism
August 8, 2019. The Wall Lab has released a new study in Cell, entitled "Inherited and De Novo Genetic Risk for Autism Impacts Shared Networks. " This is the largest whole genome sequencing study of its kind in this space, with some great discoveries including a new syndrome for autism.
Large dataset enables prediction of repair after CRISPR-Cas9 editing in primary T cells
July 29, 2019. This study, published in Nature Biotechnology, features work by DBDS Faculty member, James Zou, and members of the Zou Group.
Wall Lab research featured in New York Times!
July 17, 2019. Read more about how "Google Glass May Have an Afterlife as a Device to Teach Autistic Children."
5 Questions: Genevieve Wojcik on the need for diversity in genome-based studies
June 20, 2019. DBDS Research Scientist, Dr. Gen Wojcik, "speaks about the lack of diversity in genomewide association studies, why it’s a problem and how increasing diversity in these studies can elevate the entire population."
Sparse discriminative latent characteristics for predicting cancer drug sensitivity from genomic features
May 28, 2019. This PLoS Computational Biology publication features the results of research conducted by DBDS Department Chair, Sylvia Plevritis, and members/alumni of her lab, Gina Bourchard and David Knowles.
Determining cell type abundance and expression from bulk tissues with digital cytometry
May 6, 2019. Stanford researchers, including Assistant Professor of Biomedical Data Science, Aaron Newman, have developed a computational platform for analyzing the molecular behavior of individual cells in tissue samples, opening the door for new discoveries, diagnostics and treatments.
An open resource for accurately benchmarking small variant and reference calls
April 1, 2019. This piece, authored by Francisco De La Vega, Adjunct Professor of Biomedical Data Science, and collaborators, was published in Nature Biotechnology!
Opportunities and Challenges for transcriptome-wide association studies
April 2019- This is a new study released by Nature Genetics, authored by members of the Rivas lab, Anshul Kundaje, David Knowles, and others.
Effect of Wearable Digital Intervention for Improving Socialization in Children With Autism Spectrum Disorder
March 25, 2019- Members of the Desai and Wall Labs, including Secondary BDS Faculty members Drs. Manisha Desai and Dennis Wall, published the results from a Randomized Clinical Trial in JAMA Pediatrics.
Mitogenomes illuminate the origin and migration patterns of the indigenous people of the Canary Islands
March 20, 2019- This publication is co-authored by Bustamante Lab alumna, Rosa Fregel-Lorenzo, and lab PI Carlos Bustamante, among others.
New publication in Nature Biotechnology on benchmarking germline, small-variant calls in human genomics
March 12, 2019. As part of the Global Alliance for Genomics and Health (GA4GH), BDS Adjunct Professor Francisco De La Vega and others present a benchmarking framework for variant calling.
New Publication in Genome Medicine on PRS
December 27, 2018. BDS faculty, Drs. Carlos D. Bustamante and Francisco De La Vega published a new study on Polygenic Risk Scores: A Biased Prediction” in Genome Medicine.
New publication from the Wall Lab in PLoS Medicine
November 27, 2018. Felicitations to the Wall Lab for yesterday's publication of a super exciting paper (IMHO) in PLoS Medicine on mobile, machine-learning based detection of autism, which received top spot/front page billing!
New primer on deep learning in Nature Genetics and paper from the Zou Group in npg Digital Medicine
November 26, 2018. Congratulations to James Zou, et al, for today's publication of a primer on deep learning in genomics in Nature Genetics, with an accompanying interactive online tutorial. We hope this will be a useful resource for the community.
Also, felicitations on the feature story released last week by Stanford Medicine's New Center about a recent DeepTag paper (published in late October in npg Digital Medicine) Study authors included BDS members, Allen Nie, Ashley Zehnder, Arturo Lopez Pineda, Manuel Rivas, Carlos D. Bustamante, and James Zou, among others.
New publication in PLOS Genetics from the Rivas Lab
May 28, 2018. BDS faculty member Dr. Manuel Rivas's work, entitled "Insights into the genetic epidemiology of Crohn's and rare diseases in the Ashkenazi Jewish population," has been published today in PLOS Genetics.
UK Biobank data opens up window into genetics of disease
April 25, 2018. "A trove of genetic records from the UK Biobank was unleashed last July, and after mining the immense data set, scientists at Stanford have found strong evidence for 27 direct links between specific genetic mutations and a variety of human diseases.
Some of these mutations have never been associated with disease before, and some even seem to confer protection against disease, making them prime drug candidates.
The team's work, published in Nature Communications, focused in on a particular type of genetic oddity that halts proper protein formation before it's complete. It's called a protein-truncating variant, or a PTV. Manuel Rivas, PhD, a biomedical data scientist at Stanford, is the corresponding author."- Hanae Armitage, SCOPE Blog (Stanford School of Medicine)
AI can be sexist and racist — it’s time to make it fair
July 18, 2018. Computer scientists must identify sources of bias, de-bias training data and develop artificial-intelligence algorithms that are robust to skews in the data, argue James Zou (Stanford BDS) and Londa Schiebinger.
BMIR and DBDS Faculty Featured in New PanCan Papers
April 12, 2018. The Cancer Genome Atlas (TCGA) PanCan effort recently released a series of 27 papers. Published in various Cell journals, these papers mark the culmination and completion of the PanCancer Atlas Initiative (PanCan) and The Cancer Genome Atlas (TCGA) consortium. Faculty from Stanford’s Center for Biomedical Informatics Research (BMIR) and the Department of Biomedical Data Science (DBDS) contributed greatly to the completion of this large-scale PanCan initiative, which spanned a decade and analyzed over 11,000 tumors from 33 of the world’s most prevalent cancers.
Germline determinants of the somatic mutation landscape in 2,642 cancer genomes
November 16, 2017- Francisco de la Vega (Adjunct Professor in DBDS) and (DBDS Chair) Carlos D. Bustamante have, for the past few years, been working on a project with the International Cancer Genome Consortium - the Pan-Cancer Analysis of Whole-Genomes (PCAWG) group. The project involves the analysis of whole-genome sequencing data from cancer tumor specimens from over 2,600 cancer patients and matched normal tissue, and hundreds of researchers around the world organized in subgroups over 14 research themes. Drs De La Vega and Bustamante have, more specifically, been participating in the analysis of germline genomes to understand how the germline variants affect, among other things, the cancer somatic mutational process.
A milestone has recently been reached in the project, with the release of the germline team's manuscript at BioRxiv. This manuscript will be submitted soon for review, and will be published in a special issue in 3-4 months, with other consortium papers.
Interpretation of Neural Networks is Fragile
Fake news for AI by AI, in other words.
November 8, 2017- DBDS's Zou group just released a paper which, for the first time, demonstrates that interpretation of machine learning predictions are extremely fragile. The team, led by Ph.D. students Amirata Ghorbani and Abubakar Abid, showed that for two images that are visually identical and that are both predicted to be, for example, malignant, the machine learning algorithm can give two completely different explanations.
This is very disconcerting in practice because in order to trust machine learning predictions, researchers typically rely on its explanation for why certain predictions are made. This work shows that the explanation itself is highly unreliable.
Mosaic Mutations in Blood DNA Sequence Are Associated with Solid Tumor Cancers
July 6, 2017- This is the first of a couple of articles that will be coming out of the Rivas Lab and appearing in npj Genomic Medicine this summer 2017.
The editors of Genomic Medicine summarize the article as follows: "Having some abnormal blood cells with mutations that shorten the coding sequence of their genes increases one’s risk for solid tumors. Mark Daly and colleagues from the Broad Institute in Cambridge, Massachusetts, USA, used large genomic databases to test whether having blood cells both with and without genetic variants predicted to shorten the encoded protein — a phenomenon known as mosaic protein-truncating variants (PTVs) — was associated with developing a range of solid-tumor cancers. They studied DNA from around 8,000 people with cancer and 6,000 healthy controls. They confirmed previous reports linking these variants to breast and ovarian cancer, and extended the association to include tumors of the brain, skin and lungs. (Other studies have also shown that mosaic PTVs precede and predict the development of leukemia.) These results broadly connect cancer to blood DNA changes."
Stanford Daily Interview with DBDS Faculty James Zou
February 3, 2017- "In this series, The Stanford Daily sits down to talk with new faculty members on campus.
Assistant professor James Zou joined Stanford’s newly created biomedical data science department in the fall, teaching CS 273B: “Deep Learning in Genomics and Biomedicine” with Anshul Kundaje, assistant professor of genetics and computer science. The Daily sat down with Zou to discuss joining the Stanford community and his interests beyond his academic discipline."