Drug Response Prediction
19 papers with code • 2 benchmarks • 2 datasets
Drug response prediction is about using computer methods to guess how someone will react to certain medicines. It involves looking at various types of data, like genes, drug structures, and medical records, to predict how well a person will respond to a particular treatment. The aim is to create personalized treatment plans for patients, ensuring they get the best results with the fewest side effects. This approach not only helps doctors choose the right medicines for each patient but also speeds up the development of new drugs by predicting their effectiveness and safety. Techniques like machine learning and deep learning are commonly used to make these predictions based on different types of data, such as genetics and medical history.
GDSC
DrugBank
Most implemented papers
MDI+: A Flexible Random Forest-Based Feature Importance Framework
We show that the MDI for a feature $X_k$ in each tree in an RF is equivalent to the unnormalized $R^2$ value in a linear regression of the response on the collection of decision stumps that split on $X_k$.
Learning Curves for Drug Response Prediction in Cancer Cell Lines
adpartin/dr-learning-curves
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In contrast, a GBDT with hyperparameter tuning exhibits superior performance as compared with both NNs at the lower range of training sizes for two of the datasets, whereas the mNN performs better at the higher range of training sizes.
ASGARD: A Single-cell Guided pipeline to Aid Repurposing of Drugs
Intercellular heterogeneity is a major obstacle to successful precision medicine.
AGMI: Attention-Guided Multi-omics Integration for Drug Response Prediction with Graph Neural Networks
Accurate drug response prediction (DRP) is a crucial yet challenging task in precision medicine.
Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images
Prediction performance of three unimodal NNs which use GE are compared to assess the contribution of data augmentation methods.
A Fair Experimental Comparison of Neural Network Architectures for Latent Representations of Multi-Omics for Drug Response Prediction
kramerlab/multi-omics_analysis
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One important parameter is the depth of integration: the point at which the latent representations are computed or merged, which can be either early, intermediate, or late.
Prediction of drug effectiveness in rheumatoid arthritis patients based on machine learning algorithms
This study introduced a Drug Response Prediction (DRP) framework with two main goals: 1) design a data processing pipeline to extract information from tabular clinical data, and then preprocess it for functional use, and 2) predict RA patient's responses to drugs and evaluate classification models' performance.
Towards a Better Model with Dual Transformer for Drug Response Prediction
For the branch of cell lines genomics, we use the multi-headed attention mechanism to globally represent the genomics sequence.
Precision Anti-Cancer Drug Selection via Neural Ranking
To address this, we developed neural ranking approaches that leverage large-scale drug response data across multiple cell lines from diverse cancer types.
Zero-shot Learning of Drug Response Prediction for Preclinical Drug Screening
drugd/msda
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In this paper, we propose a zero-shot learning solution for the DRP task in preclinical drug screening.
