<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Cheng X</submitter><funding>National Key R &amp;amp;D Program of China</funding><pagination>368</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9575288</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>20(1)</volume><pubmed_abstract>Considering 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. In 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 net</pubmed_abstract><journal>BMC medicine</journal><pubmed_title>NeRD: a multichannel neural network to predict cellular response of drugs by integrating multidimensional data.</pubmed_title><pmcid>PMC9575288</pmcid><funding_grant_id>2018YFC0910405</funding_grant_id><funding_grant_id>2017YFB0202602</funding_grant_id><funding_grant_id>2017YFC1311003</funding_grant_id><funding_grant_id>2016YFC1302500</funding_grant_id><pubmed_authors>Wen Y</pubmed_authors><pubmed_authors>He S</pubmed_authors><pubmed_authors>Dai C</pubmed_authors><pubmed_authors>Peng S</pubmed_authors><pubmed_authors>Bo X</pubmed_authors><pubmed_authors>Wang X</pubmed_authors><pubmed_authors>Cheng X</pubmed_authors></additional><is_claimable>false</is_claimable><name>NeRD: a multichannel neural network to predict cellular response of drugs by integrating multidimensional data.</name><description>Considering 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. In 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 net</description><dates><release>2022-01-01T00:00:00Z</release><publication>2022 Oct</publication><modification>2025-04-18T22:28:38.132Z</modification><creation>2025-04-07T10:14:01.401Z</creation></dates><accession>S-EPMC9575288</accession><cross_references><pubmed>36244991</pubmed><doi>10.1186/s12916-022-02549-0</doi></cross_references></HashMap>