{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T20:04:03Z","timestamp":1743019443859,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030645106"},{"type":"electronic","value":"9783030645113"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-64511-3_9","type":"book-chapter","created":{"date-parts":[[2020,12,7]],"date-time":"2020-12-07T00:04:09Z","timestamp":1607299449000},"page":"87-101","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Theoretical Foundation of the Performance of Phylogeny-Based Somatic Variant Detection"],"prefix":"10.1007","author":[{"given":"Takuya","family":"Moriyama","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seiya","family":"Imoto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Satoru","family":"Miyano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Yamaguchi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,12,7]]},"reference":[{"issue":"3","key":"9_CR1","doi-asserted-by":"publisher","first-page":"568","DOI":"10.1101\/gr.129684.111","volume":"22","author":"DC Koboldt","year":"2012","unstructured":"Koboldt, D.C., et al.: VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22(3), 568\u2013576 (2012)","journal-title":"Genome Res."},{"issue":"14","key":"9_CR2","doi-asserted-by":"publisher","first-page":"1811","DOI":"10.1093\/bioinformatics\/bts271","volume":"28","author":"CT Saunders","year":"2012","unstructured":"Saunders, C.T., et al.: Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics 28(14), 1811\u20131817 (2012)","journal-title":"Bioinformatics"},{"issue":"3","key":"9_CR3","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1038\/nbt.2514","volume":"31","author":"K Cibulskis","year":"2013","unstructured":"Cibulskis, K., et al.: Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31(3), 213\u2013219 (2013)","journal-title":"Nat. Biotechnol."},{"issue":"7","key":"9_CR4","doi-asserted-by":"publisher","first-page":"e89","DOI":"10.1093\/nar\/gkt126","volume":"41","author":"Y Shiraishi","year":"2013","unstructured":"Shiraishi, Y., et al.: An empirical Bayesian framework for somatic mutation detection from cancer genome sequencing data. Nucleic Acids Res. 41(7), e89 (2013)","journal-title":"Nucleic Acids Res."},{"issue":"23","key":"9_CR5","doi-asserted-by":"publisher","first-page":"3302","DOI":"10.1093\/bioinformatics\/btu537","volume":"30","author":"N Usuyama","year":"2014","unstructured":"Usuyama, N., et al.: HapMuC: somatic mutation calling using heterozygous germ line variants near candidate mutations. Bioinformatics 30(23), 3302\u20133309 (2014)","journal-title":"Bioinformatics"},{"issue":"8","key":"9_CR6","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1038\/s41592-018-0051-x","volume":"15","author":"S Kim","year":"2018","unstructured":"Kim, S., et al.: Strelka2: fast and accurate calling of germline and somatic variants. Nat. Methods 15(8), 591\u2013594 (2018)","journal-title":"Nat. Methods"},{"issue":"21","key":"9_CR7","doi-asserted-by":"publisher","first-page":"4247","DOI":"10.1093\/bioinformatics\/btz233","volume":"35","author":"T Moriyama","year":"2019","unstructured":"Moriyama, T., et al.: A Bayesian model integration for mutation calling through data partitioning. Bioinformatics 35(21), 4247\u20134254 (2019)","journal-title":"Bioinformatics"},{"issue":"1","key":"9_CR8","doi-asserted-by":"publisher","first-page":"1041","DOI":"10.1038\/s41467-019-09027-x","volume":"10","author":"SME Sahraeian","year":"2019","unstructured":"Sahraeian, S.M.E., et al.: Deep convolutional neural networks for accurate somatic mutation detection. Nat. Commun. 10(1), 1041 (2019)","journal-title":"Nat. Commun."},{"issue":"9","key":"9_CR9","doi-asserted-by":"publisher","first-page":"e61","DOI":"10.1093\/nar\/gkv135","volume":"43","author":"M Josephidou","year":"2015","unstructured":"Josephidou, M., et al.: multiSNV: a probabilistic approach for improving detection of somatic point mutations from multiple related tumour samples. Nucleic Acids Res. 43(9), e61 (2015)","journal-title":"Nucleic Acids Res."},{"doi-asserted-by":"crossref","unstructured":"Moriyama, T., et al.: Accurate and flexible bayesian mutation call from multi-regional tumor samples. In: Mathematical and Computational Oncology, pp. 47\u201361. Springer, Cham (2019)","key":"9_CR10","DOI":"10.1007\/978-3-030-35210-3_4"},{"issue":"7","key":"9_CR11","doi-asserted-by":"publisher","first-page":"1133","DOI":"10.1093\/bioinformatics\/btu755","volume":"31","author":"KE van Rens","year":"2015","unstructured":"van Rens, K.E., et al.: SNV-PPILP: refined SNV calling for tumor data using perfect phylogenies and ILP. Bioinformatics 31(7), 1133\u20131135 (2015)","journal-title":"Bioinformatics"},{"key":"9_CR12","doi-asserted-by":"publisher","first-page":"14114","DOI":"10.1038\/ncomms14114","volume":"8","author":"JG Reiter","year":"2017","unstructured":"Reiter, J.G., et al.: Reconstructing metastatic seeding patterns of human cancers. Nat. Commun. 8, 14114 (2017)","journal-title":"Nat. Commun."},{"issue":"1","key":"9_CR13","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s42003-019-0291-z","volume":"2","author":"F Dorri","year":"2019","unstructured":"Dorri, F., et al.: Somatic mutation detection and classification through probabilistic integration of clonal population information. Commun. Biol. 2(1), 44 (2019)","journal-title":"Commun. Biol."},{"doi-asserted-by":"crossref","unstructured":"Detering, H., et al.: Accuracy of somatic variant detection in multiregional tumor sequencing data. bioRxiv 655605 (2019)","key":"9_CR14","DOI":"10.1101\/655605"},{"issue":"1","key":"9_CR15","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1002\/net.3230210104","volume":"21","author":"D Gusfield","year":"1991","unstructured":"Gusfield, D.: Efficient algorithms for inferring evolutionary trees. Networks 21(1), 19\u201328 (1991)","journal-title":"Networks"},{"doi-asserted-by":"crossref","unstructured":"Kimura, M.: The number of heterozygous nucleotide sites maintained in a finite population due to steady flux of mutations. Genetics 61(4), 126 (1969)","key":"9_CR16","DOI":"10.1093\/genetics\/61.4.893"},{"issue":"1","key":"9_CR17","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1186\/s13059-017-1311-2","volume":"18","author":"H Zafar","year":"2017","unstructured":"Zafar, H., et al.: SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models. Genome Biol. 18(1), 178 (2017)","journal-title":"Genome Biol."},{"doi-asserted-by":"crossref","unstructured":"Zafar, H., et al.: SiCloneFit: bayesian inference of population structure, genotype, and phylogeny of tumor clones from single-cell genome sequencing data. Genome Research (2019)","key":"9_CR18","DOI":"10.1101\/394262"},{"issue":"17","key":"9_CR19","doi-asserted-by":"publisher","first-page":"i671","DOI":"10.1093\/bioinformatics\/bty589","volume":"34","author":"M El-Kebir","year":"2018","unstructured":"El-Kebir, M.: SPhyR: tumor phylogeny estimation from single-cell sequencing data under loss and error. Bioinformatics 34(17), i671\u2013i679 (2018)","journal-title":"Bioinformatics"}],"container-title":["Lecture Notes in Computer Science","Mathematical and Computational Oncology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-64511-3_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T20:15:40Z","timestamp":1619295340000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-64511-3_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030645106","9783030645113"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-64511-3_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"7 December 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISMCO","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Mathematical and Computational Oncology","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"San Diego, CA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ismco2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ismco.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The symposium was held virtually due to the COVID-19 pandemic. 1 invited talk is also included.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}