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Classification of drug response using machine learning
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.
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)
Python code to build any Deep Neural Net from scratch based on Deeplearning.ai first 3 courses.
DRPreter: Interpretable Anticancer Drug Response Prediction Using Knowledge-Guided Graph Neural Networks and Transformer
Deep Generative Models for Drug Combination (Graph Set) Generation given Hierarchical Disease Network Embedding
2020 A Machine Learning Method for Drug Combination Prediction
Drug Response Variational Autoencoder
Explore Graph Convolutional Networks
Demonstration R code to test for differential sensitivity of cancer cell lines to drugs, explained by genetic information
Initial Commit
MSE5050/7050 Materials Informatics course at the University of Utah
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"
Generate molecular fingerprints using RDKit
MOLI: Multi-Omics Late Integration with deep neural networks for drug response prediction
Latex code for making neural networks diagrams
Strategies for Pre-training Graph Neural Networks
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