Profile

Add Stanford Profiles (CAP) to Your Web Pages

Description

The Profile component provides a way to add individual profiles or create lists of  profiles using information from Stanford Profiles or by creating a custom profile.

How it works

The component pulls information directly from the Stanford Profiles system (also known as CAP). Authors may search for profiles in the component using an individual's name or SUnet ID. Information available to the component includes title (several options), bio, publications and photo. Information will only display if it is listed in the individual's profile.

Please note: The component only accesses publicly available profiles. If the profile owner has set their profile to the "Stanford Only" view, their information will not appear in the component. The profile owner may edit this  setting in Profiles. 

Custom Profiles

In June 2019 an option to create a 'custom profile' was added to the component. A custom profile has the same display options as a CAP profile, thereby enabling authors to create cohesive looking people pages. Each custom profile is single use  and must be copied and pasted or recreated if used in more than one location on a website. It is not possible to use a custom profile on multiple websites. 

Custom Profile options include name, title, contact information, bio and photo.

Note: Custom profiles may not be included in lists.

Component Features

  • Display a single profile or a list(s) of profiles from Stanford Profiles
  • Create custom profiles for individuals not listed in Stanford Profiles
  • Customize what profile information is displayed
  • Select full or compact view for display

Single Profiles

Select Profile Options

First, select whether you will publish a single profile or a list of profiles. Then, choose the information that will be displayed in your profiles.

Select Profile Type

Use Stanford Profiles or create a custom profile. Stanford profiles is strongly preferred as it is updated automatically via CAP. Custom profiles may not be used in lists.

Select Title Display

When using Stanford Profiles you have several title options from which to choose. Or, you can create a custom title for use in AEM.

Example

The Carl and Elizabeth Naumann Professorship for the Dean of the School of Medicine, Professor of Otolaryngology—Head & Neck Surgery and, by courtesy, of Neurobiology and Bioengineering

Custom Profile

Create Custom Profile

Create a custom profile by adding name, title, contact information and a photo. 

Note: the Custom Profile name will not be linked.

Example

Profile Name
Profile Title

 Publications

Using the profile component to create publications lists

Profile Selection

Note that it is important to follow the steps in order. 

Step 1: Check the publications box. All publications lists include the researcher's name and title by default. You may de-select contact info and bio if you do not wish to include them in the publications list. 

Step 2: By default all publication types will appear in your list. You may create a filtered publication list by selecting from the following options in the drop down: 'featured publications', 'Journal Articles', 'Books and Book Chapters', 'Conference Proceedings'

Step 3: The default number of publications to appear in a list is five.  A link to all of the researcher's publications is included at the bottom of each list.

Step 4: Select 'Single Profile'. Publications lists can ONLY be created using a single profile.

Profile Type and Title

Use the default selection 'Use Stanford Profiles'.

Search for the name of the researcher.

Select the type of title that will accompany the researcher's name at the top of the list. 

Publications list example

Kenneth Fong Professor and Professor of Bioengineering, of Genetics, of Medicine (General Medical Discipline), of Biomedical Data Science and, by courtesy, of Computer Science

Publications

  • Distinct clinical phenotypes for Crohn's disease derived from patient surveys. BMC gastroenterology Liu, T., Han, L., Tilley, M., Afzelius, L., Maciejewski, M., Jelinsky, S., Tian, C., McIntyre, M., 23andMe Research Team, Bing, N., Hung, K., Altman, R. B., Agee, M., Auton, A., Bell, R. K., Bryc, K., Elson, S. L., Fontanillas, P., Furlotte, N. A., Hinds, D. A., Huber, K. E., Kleinman, A., Litterman, N. K., McCreight, J. C., McIntyre, M., Mountain, J. L., Noblin, E. S., Northover, C. A., Pitts, S. J., Sathirapongsasuti, J. F., Sazonova, O. V., Shelton, J. F., Shringarpure, S., Tian, C., Tung, J. Y., Vacic, V. 2021; 21 (1): 160

    Abstract

    BACKGROUND: Defining clinical phenotypes provides opportunities for new diagnostics and may provide insights into early intervention and disease prevention. There is increasing evidence that patient-derived health data may contain information that complements traditional methods of clinical phenotyping. The utility of these data for defining meaningful phenotypic groups is of great interest because social media and online resources make it possible to query large cohorts of patients with health conditions.METHODS: We evaluated the degree to which patient-reported categorical data is useful for discovering subclinical phenotypes and evaluated its utility for discovering new measures of disease severity, treatment response and genetic architecture. Specifically, we examined the responses of 1961 patients with inflammatory bowel disease to questionnaires in search of sub-phenotypes. We applied machine learning methods to identify novel subtypes of Crohn's disease and studied their associations with drug responses.RESULTS: Using the patients' self-reported information, we identified two subpopulations of Crohn's disease; these subpopulations differ in disease severity, associations with smoking, and genetic transmission patterns. We also identified distinct features of drug response for the two Crohn's disease subtypes. These subtypes show a trend towards differential genotype signatures.CONCLUSION: Our findings suggest that patient-defined data can have unplanned utility for defining disease subtypes and may be useful for guiding treatment approaches.

    View details for DOI 10.1186/s12876-021-01740-6

    View details for PubMedID 33836648

  • Opportunities and challenges for the computational interpretation of rare variation in clinically important genes. American journal of human genetics McInnes, G., Sharo, A. G., Koleske, M. L., Brown, J. E., Norstad, M., Adhikari, A. N., Wang, S., Brenner, S. E., Halpern, J., Koenig, B. A., Magnus, D. C., Gallagher, R. C., Giacomini, K. M., Altman, R. B. 2021; 108 (4): 535–48

    Abstract

    Genome sequencing is enabling precision medicine-tailoring treatment to the unique constellation of variants in an individual's genome. The impact of recurrent pathogenic variants is often understood, however there is a long tail of rare genetic variants that are uncharacterized. The problem of uncharacterized rare variation is especially acute when it occurs in genes of known clinical importance with functionally consequential variants and associated mechanisms. Variants of uncertain significance (VUSs) in these genes are discovered at a rate that outpaces current ability to classify them with databases of previous cases, experimental evaluation, and computational predictors. Clinicians are thus left without guidance about the significance of variants that may have actionable consequences. Computational prediction of the impact of rare genetic variation is increasingly becoming an important capability. In this paper, we review the technical and ethical challenges of interpreting the function of rare variants in two settings: inborn errors of metabolism in newborns and pharmacogenomics. We propose a framework for a genomic learning healthcare system with an initial focus on early-onset treatable disease in newborns and actionable pharmacogenomics. We argue that (1) a genomic learning healthcare system must allow for continuous collection and assessment of rare variants, (2) emerging machine learning methods will enable algorithms to predict the clinical impact of rare variants on protein function, and (3) ethical considerations must inform the construction and deployment of all rare-variation triage strategies, particularly with respect to health disparities arising from unbalanced ancestry representation.

    View details for DOI 10.1016/j.ajhg.2021.03.003

    View details for PubMedID 33798442

  • Large-scale labeling and assessment of sex bias in publicly available expression data. BMC bioinformatics Flynn, E., Chang, A., Altman, R. B. 2021; 22 (1): 168

    Abstract

    BACKGROUND: Women are at more than 1.5-fold higher risk for clinically relevant adverse drug events. While this higher prevalence is partially due to gender-related effects, biological sex differences likely also impact drug response. Publicly available gene expression databases provide a unique opportunity for examining drug response at a cellular level. However, missingness and heterogeneity of metadata prevent large-scale identification of drug exposure studies and limit assessments of sex bias. To address this, we trained organism-specific models to infer sample sex from gene expression data, and used entity normalization to map metadata cell line and drug mentions to existing ontologies. Using this method, we inferred sex labels for 450,371 human and 245,107 mouse microarray and RNA-seq samples from refine.bio.RESULTS: Overall, we find slight female bias (52.1%) in human samples and (62.5%) male bias in mouse samples; this corresponds to a majority of mixed sex studies in humans and single sex studies in mice, split between female-only and male-only (25.8% vs. 18.9% in human and 21.6% vs. 31.1% in mouse, respectively). In drug studies, we find limited evidence for sex-sampling bias overall; however, specific categories of drugs, including human cancer and mouse nervous system drugs, are enriched in female-only and male-only studies, respectively. We leverage our expression-based sex labels to further examine the complexity of cell line sex and assess the frequency of metadata sex label misannotations (2-5%).CONCLUSIONS: Our results demonstrate limited overall sex bias, while highlighting high bias in specific subfields and underscoring the importance of including sex labels to better understand the underlying biology. We make our inferred and normalized labels, along with flags for misannotated samples, publicly available to catalyze the routine use of sex as a study variable in future analyses.

    View details for DOI 10.1186/s12859-021-04070-2

    View details for PubMedID 33784977

  • Search and visualization of gene-drug-disease interactions for pharmacogenomics and precision medicine research using GeneDive. Journal of biomedical informatics Wong, M., Previde, P., Cole, J., Thomas, B., Laxmeshwar, N., Mallory, E., Lever, J., Petkovic, D., Altman, R. B., Kulkarni, A. 2021: 103732

    Abstract

    BACKGROUND: Understanding the relationships between genes, drugs, and disease states is at the core of pharmacogenomics. Two leading approaches for identifying these relationships in medical literature are: human expert led manual curation efforts, and modern data mining based automated approaches. The former generates small amounts of high-quality data, and the later offers large volumes of mixed quality data. The algorithmically extracted relationships are often accompanied by supporting evidence, such as, confidence scores, source articles, and surrounding contexts (excerpts) from the articles, that can used as data quality indicators. Tools that can leverage these quality indicators to help the user gain access to larger and high-quality data are needed.APPROACH: We introduce GeneDive, a web application for pharmacogenomics researchers and precision medicine practitioners that makes gene, disease, and drug interactions data easily accessible and usable. GeneDive is designed to meet three key objectives: (1) provide functionality to manage information-overload problem and facilitate easy assimilation of supporting evidence, (2) support longitudinal and exploratory research investigations, and (3) offer integration of user-provided interactions data without requiring data sharing.RESULTS: GeneDive offers multiple search modalities, visualizations, and other features that guide the user efficiently to the information of their interest. To facilitate exploratory research, GeneDive makes the supporting evidence and context for each interaction readily available and allows the data quality threshold to be controlled by the user as per their risk tolerance level. The interactive search-visualization loop enables relationship discoveries between diseases, genes, and drugs that might not be explicitly described in literature but are emergent from the source medical corpus and deductive reasoning. The ability to utilize user's data either in combination with the GeneDive native datasets or in isolation promotes richer data-driven exploration and discovery. These functionalities along with GeneDive's applicability for precision medicine, bringing the knowledge contained in biomedical literature to bear on particular clinical situations and improving patient care, are illustrated through detailed use cases.CONCLUSION: GeneDive is a comprehensive, broad-use biological interactions browser. The GeneDive application and information about its underlying system architecture are available at http://www.genedive.net. GeneDive Docker image is also available for download at this URL, allowing users to (1) import their own interaction data securely and privately; and (2) generate and test hypotheses across their own and other datasets.

    View details for DOI 10.1016/j.jbi.2021.103732

    View details for PubMedID 33737208

  • Modeling drug response using network-based personalized treatment prediction (NetPTP) with applications to inflammatory bowel disease. PLoS computational biology Han, L., Sayyid, Z. N., Altman, R. B. 2021; 17 (2): e1008631

    Abstract

    For many prevalent complex diseases, treatment regimens are frequently ineffective. For example, despite multiple available immunomodulators and immunosuppressants, inflammatory bowel disease (IBD) remains difficult to treat. Heterogeneity in the disease across patients makes it challenging to select the optimal treatment regimens, and some patients do not respond to any of the existing treatment choices. Drug repurposing strategies for IBD have had limited clinical success and have not typically offered individualized patient-level treatment recommendations. In this work, we present NetPTP, a Network-based Personalized Treatment Prediction framework which models measured drug effects from gene expression data and applies them to patient samples to generate personalized ranked treatment lists. To accomplish this, we combine publicly available network, drug target, and drug effect data to generate treatment rankings using patient data. These ranked lists can then be used to prioritize existing treatments and discover new therapies for individual patients. We demonstrate how NetPTP captures and models drug effects, and we apply our framework to individual IBD samples to provide novel insights into IBD treatment.

    View details for DOI 10.1371/journal.pcbi.1008631

    View details for PubMedID 33544718

Lists