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🧬 Process repository for my master thesis attacking drug response prediction on cancer cell-lines using bi-modal graph neural networks
This repository contains the code for the paper "Pretraining on In-Vitro and Fine-Tuning on Patient-Derived Data Improves Neural Drug Sensitivity Models for Precision Oncology"
CaDRReS-Sc is a framework for analyzing drug response heterogeneity based on single-cell RNA-seq data
Developed a chemical structure similarity matrix on Larotrectinib, the active compound in the drug Vitrakvi, using 8 other drug like compounds.
Predicting cancer drug response using parallel heterogeneous graph convolutional networks with neighborhood interactions
An algorithm for prediction of clinical drug response using preclinical cell line data in cancer
Deep Learning based Drug Response Predication with public Omics datasets
Cancer Drug Response prediction using a Recommender System
Python code for "Bayesian hybrid matrix factorisation for data integration", published at 20th International Conference on Artificial Intelligence and Statistics (AISTATS 2017).
Python implementation of deep forest method : gcForest
The Supplementary data in the paper "A Survey and Systematic Assessment of Computational Methods for Drug Response Prediction"
MOLI: Multi-Omics Late Integration with deep neural networks for drug response prediction
Simultaneous Regression and Classification Using Random Forests
All Algorithms implemented in Python
Anticancer Drug Response Prediction Using Manifold Learning
RefDNN: a reference drug based neural network for more accurate prediction of anticancer drug resistance (Scientific Reports)
R scripts included in a bioinformatics pipeline for differential PGx analysis of cancer drug response
Personalized Network Modeling of the Pan-Cancer Patient and Cell Line Interactome
Explainable Drug Sensitivity Prediction through Cancer Pathway Enrichment Scores
Reference MATLAB implementation of foundational analyses for Cancer Therapeutics Response Portal (CTRP; http://portals.broadinstitute.org/ctrp)
Classification of drug response using machine learning