Papers with Code 아카이브

Drug Response Prediction

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 2
보존된 평가 기록: 2

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.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://gitee.com/xiaoyibang/clipdrug

공식 코드

CLDR: Contrastive Learning Drug Response Models from Natural Language Supervision

https://github.com/Gaskell-1206/Ensemble_DRP

공식 코드

Prediction of drug effectiveness in rheumatoid arthritis patients based on machine learning algorithms

https://github.com/Shef-AIRE/FMM_TC

공식 코드

Foundation-Model-Boosted Multimodal Learning for fMRI-based Neuropathic Pain Drug Response Prediction

https://github.com/adpartin/cross-dataset-drp-paper

공식 코드

Benchmarking community drug response prediction models: datasets, models, tools, and metrics for cross-dataset generalization analysis

https://github.com/adpartin/dr-learning-curves

공식 코드

Learning Curves for Drug Response Prediction in Cancer Cell Lines

https://github.com/ahaubioinformatics/cetexplainer

공식 코드

Controllable Edge-Type-Specific Interpretation in Multi-Relational Graph Neural Networks for Drug Response Prediction

https://github.com/cdal-soc/predict-ai

공식 코드

Personalised Drug Identifier for Cancer Treatment with Transformers using Auxiliary Information

https://github.com/csinva/imodels

공식 코드

Integrating Random Forests and Generalized Linear Models for Improved Accuracy and Interpretability

https://github.com/drugd/msda

공식 코드

Zero-shot Learning of Drug Response Prediction for Preclinical Drug Screening

https://github.com/hliulab/scadadrug

공식 코드

Towards generalization of drug response prediction to single cells and patients utilizing importance-aware multi-source domain transfer learning

https://github.com/kramerlab/multi-omics_analysis

공식 코드

A Fair Experimental Comparison of Neural Network Architectures for Latent Representations of Multi-Omics for Drug Response Prediction

https://github.com/kyrs/wiser

공식 코드

WISER: Weak supervISion and supErvised Representation learning to improve drug response prediction in cancer

https://github.com/lanagarmire/asgard

공식 코드

ASGARD: A Single-cell Guided pipeline to Aid Repurposing of Drugs

https://github.com/likunnb/TransEDRP

공식 코드

Towards a Better Model with Dual Transformer for Drug Response Prediction

https://github.com/ninglab/drugranker

공식 코드

Precision Anti-Cancer Drug Selection via Neural Ranking

https://github.com/qsong-github/scdrugmap

공식 코드

scDrugMap: Benchmarking Large Foundation Models for Drug Response Prediction

https://github.com/vshy-dream/drexplainer

공식 코드

DRExplainer: Quantifiable Interpretability in Drug Response Prediction with Directed Graph Convolutional Network

https://github.com/xiaoqiongxia/transcdr

공식 코드

TransCDR: a deep learning model for enhancing the generalizability of cancer drug response prediction through transfer learning and multimodal data fusion for drug representation

https://github.com/yivan-wyygdsg/agmi

공식 코드

AGMI: Attention-Guided Multi-omics Integration for Drug Response Prediction with Graph Neural Networks

https://github.com/yu-group/imodels-experiments

공식 코드

Integrating Random Forests and Generalized Linear Models for Improved Accuracy and Interpretability

https://github.com/jamesdolezal/slideflow

Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.