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. 

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.


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.


Profile Name
Profile Title


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


  • Leveraging the Cell Ontology to classify unseen cell types. Nature communications Wang, S., Pisco, A. O., McGeever, A., Brbic, M., Zitnik, M., Darmanis, S., Leskovec, J., Karkanias, J., Altman, R. B. 2021; 12 (1): 5556


    Single cell technologies are rapidly generating large amounts of data that enables us to understand biological systems at single-cell resolution. However, joint analysis of datasets generated by independent labs remains challenging due to a lack of consistent terminology to describe cell types. Here, we present OnClass, an algorithm and accompanying software for automatically classifying cells into cell types that are part of the controlled vocabulary that forms the Cell Ontology. A key advantage of OnClass is its capability to classify cells into cell types not present in the training data because it uses the Cell Ontology graph to infer cell type relationships. Furthermore, OnClass can be used to identify marker genes for all the cell ontology categories, regardless of whether the cell types are present or absent in the training data, suggesting that OnClass goes beyond a simple annotation tool for single cell datasets, being the first algorithm capable to identify marker genes specific to all terms of the Cell Ontology and offering the possibility of refining the Cell Ontology using a data-centric approach.

    View details for DOI 10.1038/s41467-021-25725-x

    View details for PubMedID 34548483

  • PhenClust, a standalone tool for identifying trends within sets of biological phenotypes using semantic similarity and the Unified Medical Language System metathesaurus. JAMIA open Wilson, J. L., Wong, M., Stepanov, N., Petkovic, D., Altman, R. 2021; 4 (3): ooab079


    Objectives: We sought to cluster biological phenotypes using semantic similarity and create an easy-to-install, stable, and reproducible tool.Materials and Methods: We generated Phenotype Clustering (PhenClust)-a novel application of semantic similarity for interpreting biological phenotype associations-using the Unified Medical Language System (UMLS) metathesaurus, demonstrated the tool's application, and developed Docker containers with stable installations of two UMLS versions.Results: PhenClust identified disease clusters for drug network-associated phenotypes and a meta-analysis of drug target candidates. The Dockerized containers eliminated the requirement that the user install the UMLS metathesaurus.Discussion: Clustering phenotypes summarized all phenotypes associated with a drug network and two drug candidates. Docker containers can support dissemination and reproducibility of tools that are otherwise limited due to insufficient software support.Conclusion: PhenClust can improve interpretation of high-throughput biological analyses where many phenotypes are associated with a query and the Dockerized PhenClust achieved our objective of decreasing installation complexity.

    View details for DOI 10.1093/jamiaopen/ooab079

    View details for PubMedID 34541463

  • Genome-wide Association Studies in Pharmacogenomics. Clinical pharmacology and therapeutics McInnes, G., Yee, S. W., Pershad, Y., Altman, R. B. 2021


    The increasing availability of genotype data linked with information about drug-response phenotypes has enabled genome-wide association studies (GWAS) that uncover genetic determinants of drug response. GWAS have discovered associations between genetic variants and both drug efficacy and adverse drug reactions. Despite these successes, the design of GWAS in pharmacogenomics faces unique challenges. In this review we analyze the last decade of GWAS in pharmacogenomics. We review trends in publications over time, including the drugs and drug classes studied and the clinical phenotypes used. Several data sharing consortia have contributed substantially to the PGx GWAS literature. We anticipate increased focus on biobanks and highlight phenotypes that would best enable future pharmacogenomics discoveries.

    View details for DOI 10.1002/cpt.2349

    View details for PubMedID 34185318

  • 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


    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


    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