Research to the People
  • What is Research to the People?
  • About the Data
    • What Data Do We Work With?
    • Recommended: External Data Sources
  • Hacking on the Cloud
    • Getting Set-up on Google Cloud
    • Cloud Toolbox
  • Biology-AI Toolbox
    • Overview
  • Specialized Biological Domains
    • Overview
    • Cancer Fundamentals
    • Cancer Analysis Approaches: Bio/AI
    • SVAI Research Team MVPs
  • Biological Fundamentals
    • Overview
    • Genome Analysis: The Basics
    • Proteome Analysis: The Basics
    • Transcriptome Analysis: The Basics
    • Genomic Applications
    • Transcriptomic Applications
    • Proteomic Applications
    • Multi-omics Bioinformatic Applications
  • AI fundamentals
    • Overview
    • Computational Linear Algebra Techniques
    • Machine Learning Heuristics
    • Types of Machine Learning problems: Supervised, Unsupervised, and Reinforcement Learning
    • Fundamental ML Models
    • ML Applications
    • Networks: Another type of ML topic
    • Deep Learning Fundamentals
    • You Don't Have Enough DATA
    • CNNs: An Overview
    • RNNs: An Overview
    • GANs: An overview
    • Deep Belief Networks: Deep Dive
    • Autoencoders: Deep Dive
    • DL Applications
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On this page
  • AutoNF2 Team
  • Pipeline Walk-Through
  • DeeperDrugs
  • Pipeline Walk-Through
  • AIzheng 
  • Pipeline Walk-Through
  • DamTheRiver 
  • Pipeline Walk-Through
  • ExpressForce 
  • Pipeline Walk-Through
  1. Specialized Biological Domains

SVAI Research Team MVPs

AutoNF2 Team

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!

Pipeline Walk-Through

DeeperDrugs

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.

Pipeline Walk-Through

AIzheng 

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.

Pipeline Walk-Through

DamTheRiver 

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.

Pipeline Walk-Through

ExpressForce 

“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.

Pipeline Walk-Through

PreviousCancer Analysis Approaches: Bio/AINextOverview

Last updated 6 years ago