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
RedMed: Extending drug lexicons for social media applications.
Journal of biomedical informatics
Social media has been identified as a promising potential source of information for pharmacovigilance. The adoption of social media data has been hindered by the massive and noisy nature of the data. Initial attempts to use social media data have relied on exact text matches to drugs of interest, and therefore suffer from the gap between formal drug lexicons and the informal nature of social media. The Reddit comment archive represents an ideal corpus for bridging this gap. We trained a word embedding model, RedMed, to facilitate the identification and retrieval of health entities from Reddit data. We compare the performance of our model trained on a consumer-generated corpus against publicly available models trained on expert-generated corpora. Our automated classification pipeline achieves an accuracy of 0.88 and a specificity of > 0.9 across four different term classes. Of all drug mentions, an average of 79% (±0.5%) were exact matches to a generic or trademark drug name, 14% (±0.5%) were misspellings, 6.4% (±0.3%) were synonyms, and 0.13% (±0.05%) were pill marks. We find that our system captures an additional 20% of mentions; these would have been missed by approaches that rely solely on exact string matches. We provide a lexicon of misspellings and synonyms for 2,978 drugs and a word embedding model trained on a health-oriented subset of Reddit.
View details for DOI 10.1016/j.jbi.2019.103307
View details for PubMedID 31627020
High precision protein functional site detection using 3D convolutional neural networks.
Bioinformatics (Oxford, England)
2019; 35 (9): 1503–12
MOTIVATION: Accurate annotation of protein functions is fundamental for understanding molecular and cellular physiology. Data-driven methods hold promise for systematically deriving rules underlying the relationship between protein structure and function. However, the choice of protein structural representation is critical. Pre-defined biochemical features emphasize certain aspects of protein properties while ignoring others, and therefore may fail to capture critical information in complex protein sites.RESULTS: In this paper, we present a general framework that applies 3D convolutional neural networks (3DCNNs) to structure-based protein functional site detection. The framework can extract task-dependent features automatically from the raw atom distributions. We benchmarked our method against other methods and demonstrate better or comparable performance for site detection. Our deep 3DCNNs achieved an average recall of 0.955 at a precision threshold of 0.99 on PROSITE families, detected 98.89 and 92.88% of nitric oxide synthase and TRYPSIN-like enzyme sites in Catalytic Site Atlas, and showed good performance on challenging cases where sequence motifs are absent but a function is known to exist. Finally, we inspected the individual contributions of each atom to the classification decisions and show that our models successfully recapitulate known 3D features within protein functional sites.AVAILABILITY AND IMPLEMENTATION: The 3DCNN models described in this paper are available at https://simtk.org/projects/fscnn.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
View details for PubMedID 31051039
Graph Convolutional Neural Networks for Predicting Drug-Target Interactions.
Journal of chemical information and modeling
Accurate determination of target-ligand interactions is crucial in the drug discovery process. In this paper, we propose a graph-convolutional (Graph-CNN) framework for predicting protein-ligand interactions. First, we built an unsupervised graph-autoencoder to learn fixed-size representations of protein pockets from a set of representative druggable protein binding sites. Second, we trained two Graph-CNNs to automatically extract features from pocket graphs and 2D ligand graphs, respectively, driven by binding classification labels. We demonstrate that graph-autoencoders can learn fixed-size representations for protein pockets of varying sizes and the Graph-CNN framework can effectively capture protein-ligand binding interactions without relying on target-ligand complexes. Across several metrics, Graph-CNNs achieved better or comparable performance to 3DCNN ligand-scoring, AutoDock Vina, RF-Score, and NNScore on common virtual screening benchmark data sets. Visualization of key pocket residues and ligand atoms contributing to the classification decisions confirms that our networks are able to detect important interface residues and ligand atoms within the pockets and ligands, respectively.
View details for DOI 10.1021/acs.jcim.9b00628
View details for PubMedID 31580672
Pocket similarity identifies selective estrogen receptor modulators as microtubule modulators at the taxane site.
2019; 10 (1): 1033
Taxanes are a family of natural products with a broad spectrum of anticancer activity. This activity is mediated by interaction with the taxane site of beta-tubulin, leading to microtubule stabilization and cell death. Although widely used in the treatment of breast cancer and other malignancies, existing taxane-based therapies including paclitaxel and the second-generation docetaxel are currently limited by severe adverse effects and dose-limiting toxicity. To discover taxane site modulators, we employ a computational binding site similarity screen of > 14,000 drug-like pockets from PDB, revealing an unexpected similarity between the estrogen receptor and the beta-tubulin taxane binding pocket. Evaluation of nine selective estrogen receptor modulators (SERMs) via cellular and biochemical assays confirms taxane site interaction, microtubule stabilization, and cell proliferation inhibition. Our study demonstrates that SERMs can modulate microtubule assembly and raises the possibility of an estrogen receptor-independent mechanism for inhibiting cell proliferation.
View details for PubMedID 30833575
- A global network of biomedical relationships derived from text BIOINFORMATICS 2018; 34 (15): 2614–24 Hide More