ArticleBMC medicine2022
NeRD: a multichannel neural network to predict cellular response of drugs by integrating multidimensional data.
Article in BMC medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Foundation models and deep learning for cancer drug response prediction: a framework for data, metrics, and validation.Briefings in bioinformatics · 2026Review
- DrugSurvPlot: A Novel Web-Based Platform Harnessing Drug Sensitivity Scores as Molecular Biomarkers for Pan-Cancer Survival Prognosis.Current gene therapy · 2026Article
- Exploration of the potential therapeutic effects and targets of Coriandrum sativum on non-erosive esophagitis based on bioinformatics and molecular dynamics simulation.Scientific reports · 2025Article
- DD-PRiSM: a deep learning framework for decomposition and prediction of synergistic drug combinations.Briefings in bioinformatics · 2024Article
- TransCDR: a deep learning model for enhancing the generalizability of drug activity prediction through transfer learning and multimodal data fusion.BMC biology · 2024Article
- Multi-output prediction of dose-response curves enables drug repositioning and biomarker discovery.NPJ precision oncology · 2024Article
- A comprehensive benchmarking of machine learning algorithms and dimensionality reduction methods for drug sensitivity prediction.Briefings in bioinformatics · 2024Article
- Hi-GeoMVP: a hierarchical geometry-enhanced deep learning model for drug response prediction.Bioinformatics (Oxford, England) · 2024Article
- CPADS: a web tool for comprehensive pancancer analysis of drug sensitivity.Briefings in bioinformatics · 2024Article
- Article
- DELFOS-drug efficacy leveraging forked and specialized networks-benchmarking scRNA-seq data in multi-omics-based prediction of cancer sensitivity.Bioinformatics (Oxford, England) · 2023Article
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7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundConsidering the heterogeneity of tumors, it is a key issue in precision medicine to predict the drug response of each individual. The accumulation of various types of drug informatics and multi-omics data facilitates the development of efficient models for drug response prediction. However, the selection of high-quality data sources and the design of suitable methods remain a challenge.
methodsIn this paper, we design NeRD, a multidimensional data integration model based on the PRISM drug response database, to predict the cellular response of drugs. Four feature extractors, including drug structure extractor (DSE), molecular fingerprint extractor (MFE), miRNA expression extractor (mEE), and copy number extractor (CNE), are designed for different types and dimensions of data. A fully connected network is used to fuse all features and make predictions.
resultsExperimental results demonstrate the effective integration of the global and local structural features of drugs, as well as the features of cell lines from different omics data. For all metrics tested on the PRISM database, NeRD surpassed previous approaches. We also verified that NeRD has strong reliability in the prediction results of new samples. Moreover, unlike other algorithms, when the amount of training data was reduced, NeRD maintained stable performance.
conclusionsNeRD's feature fusion provides a new idea for drug response prediction, which is of great significance for precise cancer treatment.
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