{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Kasianov AS"],"funding":["Ministry of Science and Higher Education","Institute for Information Transmission Problems","Russian Science Foundation"],"pagination":["e1010743"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9879537"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["19(1)"],"pubmed_abstract":["Interspecific gene comparisons are the keystones for many areas of biological research and are especially important for the translation of knowledge from model organisms to economically important species. Currently they are hampered by the low resolution of methods based on sequence analysis and by the complex evolutionary history of eukaryotic genes. This is especially critical for plants, whose genomes are shaped by multiple whole genome duplications and subsequent gene loss. This requires the development of new methods for comparing the functions of genes in different species. Here, we report ISEEML (Interspecific Similarity of Expression Evaluated using Machine Learning)-a novel machine learning-based algorithm for interspecific gene classification. In contrast to previous studies focu"],"journal":["PLoS computational biology"],"pubmed_title":["Interspecific comparison of gene expression profiles using machine learning."],"pmcid":["PMC9879537"],"funding_grant_id":["17-14-01315","075-15-2021-1064","FFNU-2022-0037"],"pubmed_authors":["Penin AA","Kasianov AS","Klepikova AV","Mayorov AV","Buzanov GS","Logacheva MD"],"additional_accession":[]},"is_claimable":false,"name":"Interspecific comparison of gene expression profiles using machine learning.","description":"Interspecific gene comparisons are the keystones for many areas of biological research and are especially important for the translation of knowledge from model organisms to economically important species. Currently they are hampered by the low resolution of methods based on sequence analysis and by the complex evolutionary history of eukaryotic genes. This is especially critical for plants, whose genomes are shaped by multiple whole genome duplications and subsequent gene loss. This requires the development of new methods for comparing the functions of genes in different species. Here, we report ISEEML (Interspecific Similarity of Expression Evaluated using Machine Learning)-a novel machine learning-based algorithm for interspecific gene classification. In contrast to previous studies focu","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Jan","modification":"2025-04-25T20:12:16.192Z","creation":"2025-04-06T08:15:07.056Z"},"accession":"S-EPMC9879537","cross_references":{"pubmed":["36626392"],"doi":["10.1371/journal.pcbi.1010743"]}}