{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Cabrera-Andrade A"],"funding":["Xunta de Galicia","Instituto de Salud Carlos III","Eusko Jaurlaritza","Ikerbasque, Basque Foundation for Science","Ministerio de Econom?a y Competitividad","European Regional Development Fund"],"pagination":["27211-27220"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7594149"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["5(42)"],"pubmed_abstract":["Sarcomas are a group of malignant neoplasms of connective tissue with a different etiology than carcinomas. The efforts to discover new drugs with antisarcoma activity have generated large datasets of multiple preclinical assays with different experimental conditions. For instance, the ChEMBL database contains outcomes of 37,919 different antisarcoma assays with 34,955 different chemical compounds. Furthermore, the experimental conditions reported in this dataset include 157 types of biological activity parameters, 36 drug targets, 43 cell lines, and 17 assay organisms. Considering this information, we propose combining perturbation theory (PT) principles with machine learning (ML) to develop a PTML model to predict antisarcoma compounds. PTML models use one function of reference that meas"],"journal":["ACS omega"],"pubmed_title":["Perturbation-Theory Machine Learning (PTML) Multilabel Model of the ChEMBL Dataset of Preclinical Assays for Antisarcoma Compounds."],"pmcid":["PMC7594149"],"funding_grant_id":["IT1045-16","UNLC08-1E-002","ED431D 2017/23","ED431D 2017/16","ED431C 2018/49","ED431G/01","PI17/01826","CTQ2016-74881-P","UNLC13-13-3503"],"pubmed_authors":["Perez-Castillo Y","Lopez-Cortes A","Munteanu CR","Cabrera-Andrade A","Gonzalez-Diaz H","Pazos A","Tejera E","Arrasate S"],"additional_accession":[]},"is_claimable":false,"name":"Perturbation-Theory Machine Learning (PTML) Multilabel Model of the ChEMBL Dataset of Preclinical Assays for Antisarcoma Compounds.","description":"Sarcomas are a group of malignant neoplasms of connective tissue with a different etiology than carcinomas. The efforts to discover new drugs with antisarcoma activity have generated large datasets of multiple preclinical assays with different experimental conditions. For instance, the ChEMBL database contains outcomes of 37,919 different antisarcoma assays with 34,955 different chemical compounds. Furthermore, the experimental conditions reported in this dataset include 157 types of biological activity parameters, 36 drug targets, 43 cell lines, and 17 assay organisms. Considering this information, we propose combining perturbation theory (PT) principles with machine learning (ML) to develop a PTML model to predict antisarcoma compounds. PTML models use one function of reference that meas","dates":{"release":"2020-01-01T00:00:00Z","publication":"2020 Oct","modification":"2026-04-07T23:26:34.978Z","creation":"2020-11-07T10:15:09Z"},"accession":"S-EPMC7594149","cross_references":{"pubmed":["33134682"],"doi":["10.1021/acsomega.0c03356"]}}