FarzanT
Computational Biology PhD Student @LincolnSteinLab at the University of Toronto
Ontario Institute for Cancer ResearchToronto, Canada
Pinned Repositories
BCB430Y_2017_2018
Scripts used to analyze pan-cancer data from TCGA in search for non-canonical gene expression-CNA patterns
Coursera
A set of code examples from MOOCs from Coursera
DeepSpeed
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
Farzan-rete
Network Analysis for Cell- and Systems Biology
FarzanT
Config files for my GitHub profile.
GSEA-in-R
An overview of useful GSEA tools available on Bioconductor
ICGC_Promoters
metrics
Machine learning metrics for distributed, scalable PyTorch applications.
Paradoxical_Genes
Repository for the analysis of anti-correlated gene expression and CNA data patterns
MMDRP
Drug Response Prediction and Biomarker Discovery Using Multi-Modal Deep Learning
FarzanT's Repositories
FarzanT/BCB430Y_2017_2018
Scripts used to analyze pan-cancer data from TCGA in search for non-canonical gene expression-CNA patterns
FarzanT/Coursera
A set of code examples from MOOCs from Coursera
FarzanT/DeepSpeed
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
FarzanT/Farzan-rete
Network Analysis for Cell- and Systems Biology
FarzanT/FarzanT
Config files for my GitHub profile.
FarzanT/GSEA-in-R
An overview of useful GSEA tools available on Bioconductor
FarzanT/ICGC_Promoters
FarzanT/metrics
Machine learning metrics for distributed, scalable PyTorch applications.
FarzanT/Paradoxical_Genes
Repository for the analysis of anti-correlated gene expression and CNA data patterns
FarzanT/pytorch_geometric
Geometric Deep Learning Extension Library for PyTorch
FarzanT/ray
An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.
FarzanT/Reuters-21578-Classification
Text classification with Reuters-21578 datasets using Gensim Word2Vec and Keras LSTM