SVAI Research Team MVPs
Last updated
Last updated
Led by Jyotika Varshney, DVM, PhD, used an unsupervised transfer learning approach to identify novel drug targets for NF2, also identifying miR-200a as a potential circulating marker for NF2.
Applying deep learning to identify potential novel therapeutic targets!
Implementation of rigorous variant filtering and target pruning, including a CRISPR/Cas9 repair design, that pipelines into drug discovery with deep learning, which includes training a DeepChem graph convolutional model, searching for optimal hyperparameters, and applying downstream experimental verification.
Modeled TCGA-RCC tumors as a “time series” across stage (Normal, I, II, III, IV) for each subtype of p1RCC: KIRC, KIRP, KICH. This team evaluated ability of a neural network to discriminate across subtype and stage, constructed stage-specific co-expression networks, and finally identified shared gene interaction communities across each tumor stage.
Identified of neo-antigens present within patent P1RCC sequence data by machine learning major histocompatibility complex affinity tool. This team implemented a very clean, sophisticated pipeline that ultimately identified 25 peptides with high MHC binding affinity.
“Nexflix For Genes”: Provided candidate biomarkers for p1RCC via a collaborative filtering using probability matrix factorization after obtaining data from COSMIC, a well-known online cancer catalogue. As an overview, this team created a Sample ID vs Gene matrix table, implemented Naive Bayes algorithm, created entity embeddings of categorical variables, and applied dimensional reduction to find candidate biomarkers for p1RCC.