<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Mohaghegh Neyshabouri M</submitter><funding>Swedish Foundation for Strategic Research</funding><funding>Horizon 2020</funding><pagination>e1010732</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9754607</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>18(12)</volume><pubmed_abstract>Identifying the interrelations among cancer driver genes and the patterns in which the driver genes get mutated is critical for understanding cancer. In this paper, we study cross-sectional data from cohorts of tumors to identify the cancer-type (or subtype) specific process in which the cancer driver genes accumulate critical mutations. We model this mutation accumulation process using a tree, where each node includes a driver gene or a set of driver genes. A mutation in each node enables its children to have a chance of mutating. This model simultaneously explains the mutual exclusivity patterns observed in mutations in specific cancer genes (by its nodes) and the temporal order of events (by its edges). We introduce a computationally efficient dynamic programming procedure for calculati</pubmed_abstract><journal>PLoS computational biology</journal><pubmed_title>ToMExO: A probabilistic tree-structured model for cancer progression.</pubmed_title><pmcid>PMC9754607</pmcid><funding_grant_id>BD15-0043</funding_grant_id><funding_grant_id>MSCA-ITN-2017-766030</funding_grant_id><pubmed_authors>Mohaghegh Neyshabouri M</pubmed_authors><pubmed_authors>Lagergren J</pubmed_authors></additional><is_claimable>false</is_claimable><name>ToMExO: A probabilistic tree-structured model for cancer progression.</name><description>Identifying the interrelations among cancer driver genes and the patterns in which the driver genes get mutated is critical for understanding cancer. In this paper, we study cross-sectional data from cohorts of tumors to identify the cancer-type (or subtype) specific process in which the cancer driver genes accumulate critical mutations. We model this mutation accumulation process using a tree, where each node includes a driver gene or a set of driver genes. A mutation in each node enables its children to have a chance of mutating. This model simultaneously explains the mutual exclusivity patterns observed in mutations in specific cancer genes (by its nodes) and the temporal order of events (by its edges). We introduce a computationally efficient dynamic programming procedure for calculati</description><dates><release>2022-01-01T00:00:00Z</release><publication>2022 Dec</publication><modification>2025-04-04T19:11:37.279Z</modification><creation>2025-04-04T19:11:37.279Z</creation></dates><accession>S-EPMC9754607</accession><cross_references><pubmed>36469540</pubmed><doi>10.1371/journal.pcbi.1010732</doi></cross_references></HashMap>