Add Stanford Profiles (CAP) to Your Web Pages
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.
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.
- 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
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.
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
Pharmacogenetics at Scale: An Analysis of the UK Biobank.
Clinical pharmacology and therapeutics
Pharmacogenetics (PGx) studies the influence of genetic variation on drug response. Clinically actionable associations inform guidelines created by the Clinical Pharmacogenetics Implementation Consortium (CPIC), but the broad impact of genetic variation on entire populations is not well-understood. We analyzed PGx allele and phenotype frequencies for 487,409 participants in the U.K. Biobank, the largest PGx study to date. For fourteen CPIC pharmacogenes known to influence human drug response, we find that 99.5% of individuals may have an atypical response to at least one drug; on average they may have an atypical response to 10.3 drugs. Nearly 24% of participants have been prescribed a drug for which they are predicted to have an atypical response. Non-European populations carry a greater frequency of variants that are predicted to be functionally deleterious; many of these are not captured by current PGx allele definitions. Strategies for detecting and interpreting rare variation will be critical for enabling broad application of pharmacogenetics.
View details for DOI 10.1002/cpt.2122
View details for PubMedID 33237584
Transfer learning enables prediction of CYP2D6 haplotype function.
PLoS computational biology
2020; 16 (11): e1008399
Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic gene whose protein product metabolizes more than 20% of clinically used drugs. Genetic variations in CYP2D6 are responsible for interindividual heterogeneity in drug response that can lead to drug toxicity and ineffective treatment, making CYP2D6 one of the most important pharmacogenes. Prediction of CYP2D6 phenotype relies on curation of literature-derived functional studies to assign a functional status to CYP2D6 haplotypes. As the number of large-scale sequencing efforts grows, new haplotypes continue to be discovered, and assignment of function is challenging to maintain. To address this challenge, we have trained a convolutional neural network to predict functional status of CYP2D6 haplotypes, called Hubble.2D6. Hubble.2D6 predicts haplotype function from sequence data and was trained using two pre-training steps with a combination of real and simulated data. We find that Hubble.2D6 predicts CYP2D6 haplotype functional status with 88% accuracy in a held-out test set and explains 47.5% of the variance in in vitro functional data among star alleles with unknown function. Hubble.2D6 may be a useful tool for assigning function to haplotypes with uncurated function, and used for screening individuals who are at risk of being poor metabolizers.
View details for DOI 10.1371/journal.pcbi.1008399
View details for PubMedID 33137098
OrderRex clinical user testing: a randomized trial of recommender system decision support on simulated cases.
Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To assess usability and usefulness of a machine learning-based order recommender system applied to simulated clinical cases.MATERIALS AND METHODS: 43 physicians entered orders for 5 simulated clinical cases using a clinical order entry interface with or without access to a previously developed automated order recommender system. Cases were randomly allocated to the recommender system in a 3:2 ratio. A panel of clinicians scored whether the orders placed were clinically appropriate. Our primary outcome included the difference in clinical appropriateness scores. Secondary outcomes included total number of orders, case time, and survey responses.RESULTS: Clinical appropriateness scores per order were comparable for cases randomized to the order recommender system (mean difference -0.11 order per score, 95% CI: [-0.41, 0.20]). Physicians using the recommender placed more orders (median 16 vs 15 orders, incidence rate ratio 1.09, 95%CI: [1.01-1.17]). Case times were comparable with the recommender system. Order suggestions generated from the recommender system were more likely to match physician needs than standard manual search options. Physicians used recommender suggestions in 98% of available cases. Approximately 95% of participants agreed the system would be useful for their workflows.DISCUSSION: User testing with a simulated electronic medical record interface can assess the value of machine learning and clinical decision support tools for clinician usability and acceptance before live deployments.CONCLUSIONS: Clinicians can use and accept machine learned clinical order recommendations integrated into an electronic order entry interface in a simulated setting. The clinical appropriateness of orders entered was comparable even when supported by automated recommendations.
View details for DOI 10.1093/jamia/ocaa190
View details for PubMedID 33106874
MARS: discovering novel cell types across heterogeneous single-cell experiments.
Although tremendous effort has been put into cell-type annotation, identification of previously uncharacterized cell types in heterogeneous single-cell RNA-seq data remains a challenge. Here we present MARS, a meta-learning approach for identifying and annotating known as well as new cell types. MARS overcomes the heterogeneity of cell types by transferring latent cell representations across multiple datasets. MARS uses deep learning to learn a cell embedding function as well as a set of landmarks in the cell embedding space. The method has a unique ability to discover cell types that have never been seen before and annotate experiments that are as yet unannotated. We apply MARS to a large mouse cell atlas and show its ability to accurately identify cell types, even when it has never seen them before. Further, MARS automatically generates interpretable names for new cell types by probabilistically defining a cell type in the embedding space.
View details for DOI 10.1038/s41592-020-00979-3
View details for PubMedID 33077966
PharmGKB tutorial for pharmacogenomics of drugs potentially used in the context of COVID-19.
Clinical pharmacology and therapeutics
Pharmacogenomics is a key area of precision medicine which is already being implemented in some health systems and may help guide clinicians towards effective therapies for individual patients. Over the last two decades, the Pharmacogenomics Knowledgebase (PharmGKB) has built a unique repository of pharmacogenomic knowledge, including annotations of clinical guideline and regulator-approved drug labels in addition to evidence-based drug pathways and annotations of the scientific literature. All of this knowledge is freely accessible on the PharmGKB website. In the first of a series of PharmGKB tutorials, we introduce the PharmGKB COVID-19 portal and, using examples of drugs found in the portal, demonstrate some of the main features of PharmGKB. This paper is intended as a resource to help users become quickly acquainted with the wealth of information stored in PharmGKB.
View details for DOI 10.1002/cpt.2067
View details for PubMedID 32978778