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2020-dili-cnn-mfe

cdsml

Classification of drug response using machine learning

css

We proposed a novel experimental design to enable a more cost-effective testing of synergy and sensitivity for a drug pair. First, the dose-response curve for each single drug is determined. Then the drug at its IC50 concentration is combined with the other drug at multiple doses, generating an IC50-based dose-response curve for the drug pair. We developed a drug combination sensitivi-ty score (CSS) to summarize the dose-response curves. Using high-throughput drug combination data from cancer cell lines, we showed that the CSS is highly reproducible among the replicates. With machine learning approaches such as elastic net, random forests and support vector ma-chines, the CSS can also be predicted at high accuracy using features including drug-target in-teraction profiles and structural fingerprints. Furthermore, we derived a synergy score based on the difference between the drug combination and the single drug dose-response curves. We showed that the CSS-based synergy score is able to detect true synergistic and antagonistic drug combinations. The IC50-based experimental design coupled with the CSS scoring facilitated the evaluation of drug combination sensitivity and synergy using the same unit, with minimal ex-perimental material that is required to achieve sufficient prediction accuracy. The experimental and computational strategy could be utilized as an efficient platform for improving the discovery rate in high-throughput drug combination screening.

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deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

deepdds

deepdr

deeph-dta

deeppurpose

A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)

dnn

Python code to build any Deep Neural Net from scratch based on Deeplearning.ai first 3 courses.

drpreter

DRPreter: Interpretable Anticancer Drug Response Prediction Using Knowledge-Guided Graph Neural Networks and Transformer

drug-combo-generator

Deep Generative Models for Drug Combination (Graph Set) Generation given Hierarchical Disease Network Embedding

drug_combination

drug_combination_prediction

2020 A Machine Learning Method for Drug Combination Prediction

drvae

Drug Response Variational Autoencoder

explore_gcn

Explore Graph Convolutional Networks

feature-selection-in-cancer-drug-response

genexdrugdemo

Demonstration R code to test for differential sensitivity of cancer cell lines to drugs, explained by genetic information

graomicdrp

graphdrp

graphdti

graphsynergy

matchmaker

Initial Commit

materialsinformatics

MSE5050/7050 Materials Informatics course at the University of Utah

mlmed_transfer_learning

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"

molecular-fingerprints

Generate molecular fingerprints using RDKit

moli

MOLI: Multi-Omics Late Integration with deep neural networks for drug response prediction

nexgb

plotneuralnet

Latex code for making neural networks diagrams

pretrain-gnns-molercular

Strategies for Pre-training Graph Neural Networks