{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wu Y"],"funding":["Research and Development Planning in Key Areas of Guangdong Province","Shanghai Science and Technology Innovation Action Planning","National Natural Science Foundation of China"],"pagination":["e1012905"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11978092"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["21(3)"],"pubmed_abstract":["Individualized prediction of cancer drug sensitivity is of vital importance in precision medicine. While numerous predictive methodologies for cancer drug response have been proposed, the precise prediction of an individual patient's response to drug and a thorough understanding of differences in drug responses among individuals continue to pose significant challenges. This study introduced a deep learning model PASO, which integrated transformer encoder, multi-scale convolutional networks and attention mechanisms to predict the sensitivity of cell lines to anticancer drugs, based on the omics data of cell lines and the SMILES representations of drug molecules. First, we use statistical methods to compute the differences in gene expression, gene mutation, and gene copy number variations be"],"journal":["PLoS computational biology"],"pubmed_title":["Anticancer drug response prediction integrating multi-omics pathway-based difference features and multiple deep learning techniques."],"pmcid":["PMC11978092"],"funding_grant_id":["62372286","2021B0202070001","20dz1203800"],"pubmed_authors":["Chen M","Qin Y","Wu Y"],"additional_accession":[]},"is_claimable":false,"name":"Anticancer drug response prediction integrating multi-omics pathway-based difference features and multiple deep learning techniques.","description":"Individualized prediction of cancer drug sensitivity is of vital importance in precision medicine. While numerous predictive methodologies for cancer drug response have been proposed, the precise prediction of an individual patient's response to drug and a thorough understanding of differences in drug responses among individuals continue to pose significant challenges. This study introduced a deep learning model PASO, which integrated transformer encoder, multi-scale convolutional networks and attention mechanisms to predict the sensitivity of cell lines to anticancer drugs, based on the omics data of cell lines and the SMILES representations of drug molecules. First, we use statistical methods to compute the differences in gene expression, gene mutation, and gene copy number variations be","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Mar","modification":"2025-07-13T03:04:57.952Z","creation":"2025-07-13T03:04:57.952Z"},"accession":"S-EPMC11978092","cross_references":{"pubmed":["40163555"],"doi":["10.1371/journal.pcbi.1012905"]}}