hossein-fallahi Goto Github PK

6-months-data-science-roadmap-

academicpages.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

advancesyntoolkit

Toolkit for Modelling and Simulation of Gene Expressions and Metabolism

aml-survival-analysis

Trying to get best of data for providing guidelines for health system

atac_peak

Deep learning framework to predict TF binding from DNA-sequence and ATAC-seq signals.

awesome-deep-vision

A curated list of deep learning resources for computer vision

awesome-single-cell

Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.

beautify-github-profile

This repository helps you to have a more beautiful and attractive github profile, and you can access a complete set of tools and guides for beautifying your github profile. 🪄 ⭐

bioinformatics

:microscope: Path to a free self-taught education in Bioinformatics!

bioinformatics-workshops

Workshops presented by the Gladstone Bioinformatics Core

biome_bacterial-genomics-tutorial

biomedical

This is the code for "How to Build a Biomedical Startup" by Siraj Raval on Youtube

breast-cancer-gene-expression

This project aims to predict people who will survive breast cancer using machine learning models with the help of clinical data and gene expression profiles of the patients.

breast-cancer-genes

Predicting survival outcome in breast cancer patients based on their gene expression

cancer-clust

Classifying RNA-Seq gene expression data by tumor type using unsupervised machine learning techniques.

cancer_drug_response

cancertypeprediction

This is the repository for paper titled as "Convolutional neural network models for cancer type prediction based on gene expression".

cap5510

CAP5510 - Bioinformatics Study and comparison of algorithms for cancer prediction using gene expression data.

celltypedecoder

The input of TumorDecon software is the gene expression profile of the tumor, and the output is the relative number of each cell type.

chip-seq_preprocess

A preprocessing pipeline for ChIP-seq, including alignment, quality control, and visualization.

comp_bio_

cs334

Algorithms of Machine Learning

cs50

Resources for students in my CS50 section at Harvard College

cstar_nature

Scripts used in the paper "Control of cell state transitions" by Rukhlenko et al.

curatedovariandata

The curatedOvarianData package provides data for gene expression analysis in patients with ovarian cancer

data-driven-mathematical-model-of-folfiri-treatment-for-colon-cancer

data-driven-qsp-software-for-personalized-colon-cancer-treatment

Colon cancer is the third leading cause of cancer-related deaths in the United States in both men and women. A major clinical challenge is to obtain an effective treatment strategy for each patient or at least identify a subset of patients who could benefit from a particular treatment. Since each colon cancer has its own unique features, it is very important to obtain personalized cancer treatments and find a way to tailor treatment strategies for each patient based on each individual's characteristics, including race, gender, genetic factors, immune response variations. Recently, Quantitative and Systems Pharmacology (QSP) has been commonly used to discover, validate, and test drugs. QSP models are a system of differential equations that model the dynamic interactions between drug(s) and a biological system. These mathematical models provide an integrated “systems level” approach to determining mechanisms of action of drugs and finding new ways to alter complex cellular networks with mono or combination therapy to obtain effective treatments. Since QSP models are a complex system of nonlinear equations with many unknown parameters, estimating the values of the model's parameters is extremely difficult. Existing parameter estimation methods for QSP models often use assembled data from various sources rather than a single curated dataset. These datasets are usually obtained through various biological experiments, in vitro and in vivo animal studies, thus rendering QSP models hard to be practicable for personalized treatments. To the best of our knowledge, no QSP model has been developed for personalized colon cancer treatments. In this project, we propose a unique approach to develop a data-driven QSP software to suggest effective treatment for each patient based on gene expression data from the primary tumor samples. Since signatures of main characteristics of tumors, such as immune response variations, can be found in gene expression profiling of primary tumors, we use gene expression data as input. We develop an innovative framework to systematically employ a combination of data science, mathematical, and statistical methods to obtain personalized colon cancer treatment. We will use these techniques to propose an optimal treatment strategy for each patient and predict the efficacy of the proposed treatment. The model might also suggest alternative therapies in case of low efficacy for some patients.

deep-learning-genomics-primer

Contains files for the deep learning in genomics primer.

deep_learning_examples

Examples of using deep learning in Bioinformatics

diamantopoulou-ctc-dynamics