{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:40Z","timestamp":1772138080037,"version":"3.50.1"},"reference-count":49,"publisher":"Oxford University Press (OUP)","issue":"18","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,9,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Summary: We devise a novel inference algorithm to effectively solve the cancer progression model reconstruction problem. Our empirical analysis of the accuracy and convergence rate of our algorithm, CAncer PRogression Inference (CAPRI), shows that it outperforms the state-of-the-art algorithms addressing similar problems.<\/jats:p>\n                  <jats:p>Motivation: Several cancer-related genomic data have become available (e.g. The Cancer Genome Atlas, TCGA) typically involving hundreds of patients. At present, most of these data are aggregated in a cross-sectional fashion providing all measurements at the time of diagnosis. Our goal is to infer cancer \u2018progression\u2019 models from such data. These models are represented as directed acyclic graphs (DAGs) of collections of \u2018selectivity\u2019 relations, where a mutation in a gene A \u2018selects\u2019 for a later mutation in a gene B. Gaining insight into the structure of such progressions has the potential to improve both the stratification of patients and personalized therapy choices.<\/jats:p>\n                  <jats:p>Results: The CAPRI algorithm relies on a scoring method based on a probabilistic theory developed by Suppes, coupled with bootstrap and maximum likelihood inference. The resulting algorithm is efficient, achieves high accuracy and has good complexity, also, in terms of convergence properties. CAPRI performs especially well in the presence of noise in the data, and with limited sample sizes. Moreover CAPRI, in contrast to other approaches, robustly reconstructs different types of confluent trajectories despite irregularities in the data. We also report on an ongoing investigation using CAPRI to study atypical Chronic Myeloid Leukemia, in which we uncovered non trivial selectivity relations and exclusivity patterns among key genomic events.<\/jats:p>\n                  <jats:p>Availability and implementation: CAPRI is part of the TRanslational ONCOlogy R package and is freely available on the web at: http:\/\/bimib.disco.unimib.it\/index.php\/Tronco<\/jats:p>\n                  <jats:p>Contact: \u00a0daniele.ramazzotti@disco.unimib.it<\/jats:p>\n                  <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btv296","type":"journal-article","created":{"date-parts":[[2015,5,13]],"date-time":"2015-05-13T21:02:46Z","timestamp":1431550966000},"page":"3016-3026","source":"Crossref","is-referenced-by-count":93,"title":["CAPRI: efficient inference of cancer progression models from cross-sectional data"],"prefix":"10.1093","volume":"31","author":[{"given":"Daniele","family":"Ramazzotti","sequence":"first","affiliation":[{"name":"1 Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy, 2Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, CA, USA, 3Courant Institute of Mathematical Sciences, New York University, New York, NY, USA and 4SYSBIO Centre of Systems Biology, Milano, Italy"}]},{"given":"Giulio","family":"Caravagna","sequence":"additional","affiliation":[{"name":"1 Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy, 2Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, CA, USA, 3Courant Institute of Mathematical Sciences, New York University, New York, NY, USA and 4SYSBIO Centre of Systems Biology, Milano, Italy"}]},{"given":"Loes","family":"Olde Loohuis","sequence":"additional","affiliation":[{"name":"1 Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy, 2Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, CA, USA, 3Courant Institute of Mathematical Sciences, New York University, New York, NY, USA and 4SYSBIO Centre of Systems Biology, Milano, Italy"}]},{"given":"Alex","family":"Graudenzi","sequence":"additional","affiliation":[{"name":"1 Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy, 2Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, CA, USA, 3Courant Institute of Mathematical Sciences, New York University, New York, NY, USA and 4SYSBIO Centre of Systems Biology, Milano, Italy"}]},{"given":"Ilya","family":"Korsunsky","sequence":"additional","affiliation":[{"name":"1 Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy, 2Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, CA, USA, 3Courant Institute of Mathematical Sciences, New York University, New York, NY, USA and 4SYSBIO Centre of Systems Biology, Milano, Italy"}]},{"given":"Giancarlo","family":"Mauri","sequence":"additional","affiliation":[{"name":"1 Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy, 2Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, CA, USA, 3Courant Institute of Mathematical Sciences, New York University, New York, NY, USA and 4SYSBIO Centre of Systems Biology, Milano, Italy"},{"name":"1 Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy, 2Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, CA, USA, 3Courant Institute of Mathematical Sciences, New York University, New York, NY, USA and 4SYSBIO Centre of Systems Biology, Milano, Italy"}]},{"given":"Marco","family":"Antoniotti","sequence":"additional","affiliation":[{"name":"1 Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy, 2Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, CA, USA, 3Courant Institute of Mathematical Sciences, New York University, New York, NY, USA and 4SYSBIO Centre of Systems Biology, Milano, Italy"}]},{"given":"Bud","family":"Mishra","sequence":"additional","affiliation":[{"name":"1 Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy, 2Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, CA, USA, 3Courant Institute of Mathematical Sciences, New York University, New York, NY, USA and 4SYSBIO Centre of Systems Biology, Milano, 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