Name: Hossein Fallahi
Type: User
Company: Razi University
Bio: I am a Molecular biologist interested in Bioinformatics. I am exploring Bio-Med data related to cancers and stem cells using AI, deep learning and ML.
Location: Kermanshah, Iran
Blog: https://hossein-fallahi.github.io/Fallahi-Bioinformatics-Lab/
Hossein Fallahi's Projects
Quantify repeat element enrichment with next-generation sequencing data
Differential analysis for ChIP-seq with biological replicates
a simple, fast and complete app for analyzing the effect of individual drugs and their combinations
Source code to reproduce results from "Exploring Drivers of Gene Expression in The Cancer Genome Atlas" by Rau et al. (2017)
Content for Epigenimcs 2020 workshop
Biomed data analysis
Explore RNA-Sequencing results in fibroblast tissues
Reproducible machine learning analysis of gene expression and alternative splicing data
Analysis code for "Perturbation-response genes reveal signaling footprints in cancer gene expression"
An evolutionary Algorithm for the Identification and Study of Prognostic Gene Expression Signatures in Cancer
A workflow for construction of Gene Expression count Matrices (GEMs). Useful for Differential Gene Expression (DGE) analysis and Gene Co-Expression Network (GCN) construction
A shiny app that obtains the coordinates and description of an input gene list (Ensembl v90) using biomaRt package.
Analysis of RNA seq data to explore gene expression in different types of cancer
TL with CNN for cancer survival prediction using gene-expression data
Generating Covid-19 Analytics Report PDFS with Python
An R package to calculate synergy for four arm in vivo experiment
(Pre-release) An easy-to-use, flexible website template for labs, with automatic citations, GitHub tag imports, pre-built components, and more
Codes of "Identifying core dynamic network biomarkers to prevent skin damage from UVB repeated exposure"
resources for learning bioinformatics
LMSM: a modular approach for identifying lncRNA related miRNA sponge modules in breast cancer
Use TCGA data to predict early/late pathologic stage of breast cancer with gene expression data using classification machine learning algorithm, train and test with multiple models, screen and evaluate significant genes from the model