{"id":"https://openalex.org/W4375862099","doi":"https://doi.org/10.1186/s12859-023-05304-1","title":"Model selection and robust inference of mutational signatures using Negative Binomial non-negative matrix factorization","display_name":"Model selection and robust inference of mutational signatures using Negative Binomial non-negative matrix factorization","publication_year":2023,"publication_date":"2023-05-08","ids":{"openalex":"https://openalex.org/W4375862099","doi":"https://doi.org/10.1186/s12859-023-05304-1","pmid":"https://pubmed.ncbi.nlm.nih.gov/37158829"},"language":"en","primary_location":{"id":"doi:10.1186/s12859-023-05304-1","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1186/s12859-023-05304-1","pdf_url":"https://link.springer.com/content/pdf/10.1186/s12859-023-05304-1.pdf","source":{"id":"https://openalex.org/S19032547","display_name":"BMC Bioinformatics","issn_l":"1471-2105","issn":["1471-2105"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BMC Bioinformatics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://link.springer.com/content/pdf/10.1186/s12859-023-05304-1.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5011213946","display_name":"Marta Pelizzola","orcid":"https://orcid.org/0000-0001-6909-2335"},"institutions":[{"id":"https://openalex.org/I204337017","display_name":"Aarhus University","ror":"https://ror.org/01aj84f44","country_code":"DK","type":"education","lineage":["https://openalex.org/I204337017"]}],"countries":["DK"],"is_corresponding":true,"raw_author_name":"Marta Pelizzola","raw_affiliation_strings":["Department of Mathematics, Aarhus University, Aarhus, Denmark. marta@math.au.dk","Department of Mathematics, Aarhus University, Aarhus, Denmark"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, Aarhus University, Aarhus, Denmark. marta@math.au.dk","institution_ids":["https://openalex.org/I204337017"]},{"raw_affiliation_string":"Department of Mathematics, Aarhus University, Aarhus, Denmark","institution_ids":["https://openalex.org/I204337017"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015309944","display_name":"Ragnhild Laursen","orcid":null},"institutions":[{"id":"https://openalex.org/I204337017","display_name":"Aarhus University","ror":"https://ror.org/01aj84f44","country_code":"DK","type":"education","lineage":["https://openalex.org/I204337017"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Ragnhild Laursen","raw_affiliation_strings":["Department of Mathematics, Aarhus University, Aarhus, Denmark"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, Aarhus University, Aarhus, Denmark","institution_ids":["https://openalex.org/I204337017"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076669546","display_name":"Asger Hobolth","orcid":"https://orcid.org/0000-0003-4056-1286"},"institutions":[{"id":"https://openalex.org/I204337017","display_name":"Aarhus University","ror":"https://ror.org/01aj84f44","country_code":"DK","type":"education","lineage":["https://openalex.org/I204337017"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Asger Hobolth","raw_affiliation_strings":["Department of Mathematics, Aarhus University, Aarhus, Denmark"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, Aarhus University, Aarhus, Denmark","institution_ids":["https://openalex.org/I204337017"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5011213946"],"corresponding_institution_ids":["https://openalex.org/I204337017"],"apc_list":{"value":1690,"currency":"GBP","value_usd":2072},"apc_paid":{"value":1690,"currency":"GBP","value_usd":2072},"fwci":1.4653,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.83877919,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"24","issue":"1","first_page":"187","last_page":"187"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10261","display_name":"Genetic Associations and Epidemiology","score":0.25459998846054077,"subfield":{"id":"https://openalex.org/subfields/1311","display_name":"Genetics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10261","display_name":"Genetic Associations and Epidemiology","score":0.25459998846054077,"subfield":{"id":"https://openalex.org/subfields/1311","display_name":"Genetics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11213","display_name":"Genomic variations and chromosomal abnormalities","score":0.2142000049352646,"subfield":{"id":"https://openalex.org/subfields/1311","display_name":"Genetics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11287","display_name":"Cancer Genomics and Diagnostics","score":0.17010000348091125,"subfield":{"id":"https://openalex.org/subfields/1306","display_name":"Cancer Research"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6331498026847839},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.6075719594955444},{"id":"https://openalex.org/keywords/negative-binomial-distribution","display_name":"Negative binomial distribution","score":0.5984925627708435},{"id":"https://openalex.org/keywords/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.5911095142364502},{"id":"https://openalex.org/keywords/computational-biology","display_name":"Computational biology","score":0.5099896192550659},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.49851465225219727},{"id":"https://openalex.org/keywords/dna-microarray","display_name":"DNA microarray","score":0.46928107738494873},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.4567287564277649},{"id":"https://openalex.org/keywords/factorization","display_name":"Factorization","score":0.42389559745788574},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.3999883532524109},{"id":"https://openalex.org/keywords/genetics","display_name":"Genetics","score":0.39622899889945984},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.35460418462753296},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3470098674297333},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3220575451850891},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.29350119829177856},{"id":"https://openalex.org/keywords/poisson-distribution","display_name":"Poisson distribution","score":0.17164182662963867},{"id":"https://openalex.org/keywords/gene","display_name":"Gene","score":0.11811086535453796},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.11171674728393555}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6331498026847839},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.6075719594955444},{"id":"https://openalex.org/C199335787","wikidata":"https://www.wikidata.org/wiki/Q743364","display_name":"Negative binomial distribution","level":3,"score":0.5984925627708435},{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.5911095142364502},{"id":"https://openalex.org/C70721500","wikidata":"https://www.wikidata.org/wiki/Q177005","display_name":"Computational biology","level":1,"score":0.5099896192550659},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49851465225219727},{"id":"https://openalex.org/C95371953","wikidata":"https://www.wikidata.org/wiki/Q591745","display_name":"DNA microarray","level":4,"score":0.46928107738494873},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.4567287564277649},{"id":"https://openalex.org/C187834632","wikidata":"https://www.wikidata.org/wiki/Q188804","display_name":"Factorization","level":2,"score":0.42389559745788574},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.3999883532524109},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.39622899889945984},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.35460418462753296},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3470098674297333},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3220575451850891},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.29350119829177856},{"id":"https://openalex.org/C100906024","wikidata":"https://www.wikidata.org/wiki/Q205692","display_name":"Poisson distribution","level":2,"score":0.17164182662963867},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.11811086535453796},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.11171674728393555},{"id":"https://openalex.org/C150194340","wikidata":"https://www.wikidata.org/wiki/Q26972","display_name":"Gene expression","level":3,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001940","descriptor_name":"Breast","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001940","descriptor_name":"Breast","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001940","descriptor_name":"Breast","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D003198","descriptor_name":"Computer Simulation","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D003198","descriptor_name":"Computer Simulation","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D003198","descriptor_name":"Computer Simulation","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008297","descriptor_name":"Male","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008297","descriptor_name":"Male","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008297","descriptor_name":"Male","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D009154","descriptor_name":"Mutation","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D009154","descriptor_name":"Mutation","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D009154","descriptor_name":"Mutation","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016010","descriptor_name":"Binomial Distribution","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016010","descriptor_name":"Binomial Distribution","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016010","descriptor_name":"Binomial Distribution","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":6,"locations":[{"id":"doi:10.1186/s12859-023-05304-1","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1186/s12859-023-05304-1","pdf_url":"https://link.springer.com/content/pdf/10.1186/s12859-023-05304-1.pdf","source":{"id":"https://openalex.org/S19032547","display_name":"BMC Bioinformatics","issn_l":"1471-2105","issn":["1471-2105"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BMC Bioinformatics","raw_type":"journal-article"},{"id":"pmid:37158829","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37158829","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BMC bioinformatics","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:10165836","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10165836","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10165836/pdf/12859_2023_Article_5304.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"BMC Bioinformatics","raw_type":"Text"},{"id":"pmh:oai:doaj.org/article:5ac27b7730f44738a081069ae12fc69a","is_oa":true,"landing_page_url":"https://doaj.org/article/5ac27b7730f44738a081069ae12fc69a","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"BMC Bioinformatics, Vol 24, Iss 1, Pp 1-24 (2023)","raw_type":"article"},{"id":"pmh:oai:pure.atira.dk:openaire/6a746bbf-152a-42a8-b392-8ac9531dfa03","is_oa":true,"landing_page_url":"https://www.scopus.com/pages/publications/85158131981","pdf_url":null,"source":{"id":"https://openalex.org/S4306400063","display_name":"Scopus (Elsevier)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Pelizzola, M, Laursen, R & Hobolth, A 2023, 'Model selection and robust inference of mutational signatures using Negative Binomial non-negative matrix factorization', BMC Bioinformatics, vol. 24, no. 1, 187. https://doi.org/10.1186/s12859-023-05304-1","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:pure.atira.dk:publications/6a746bbf-152a-42a8-b392-8ac9531dfa03","is_oa":true,"landing_page_url":"https://pure.au.dk/portal/en/publications/6a746bbf-152a-42a8-b392-8ac9531dfa03","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Pelizzola, M, Laursen, R & Hobolth, A 2023, 'Model selection and robust inference of mutational signatures using Negative Binomial non-negative matrix factorization', BMC Bioinformatics, vol. 24, no. 1, 187. https://doi.org/10.1186/s12859-023-05304-1","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"doi:10.1186/s12859-023-05304-1","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1186/s12859-023-05304-1","pdf_url":"https://link.springer.com/content/pdf/10.1186/s12859-023-05304-1.pdf","source":{"id":"https://openalex.org/S19032547","display_name":"BMC Bioinformatics","issn_l":"1471-2105","issn":["1471-2105"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BMC Bioinformatics","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4375862099.pdf","grobid_xml":"https://content.openalex.org/works/W4375862099.grobid-xml"},"referenced_works_count":42,"referenced_works":["https://openalex.org/W1882772761","https://openalex.org/W1902027874","https://openalex.org/W1995837988","https://openalex.org/W2008255210","https://openalex.org/W2009720434","https://openalex.org/W2039844283","https://openalex.org/W2057765245","https://openalex.org/W2073805525","https://openalex.org/W2085239571","https://openalex.org/W2098126593","https://openalex.org/W2114508388","https://openalex.org/W2129136620","https://openalex.org/W2130902307","https://openalex.org/W2152061559","https://openalex.org/W2158065648","https://openalex.org/W2323052220","https://openalex.org/W2344657883","https://openalex.org/W2441559604","https://openalex.org/W2512945549","https://openalex.org/W2547187744","https://openalex.org/W2611387862","https://openalex.org/W2765821397","https://openalex.org/W2782426754","https://openalex.org/W2884704125","https://openalex.org/W2890894438","https://openalex.org/W2898789672","https://openalex.org/W2951272706","https://openalex.org/W2973051835","https://openalex.org/W2983859156","https://openalex.org/W3004480399","https://openalex.org/W3006500278","https://openalex.org/W3021690585","https://openalex.org/W3034303830","https://openalex.org/W3043735364","https://openalex.org/W3093670775","https://openalex.org/W3121908401","https://openalex.org/W3166192807","https://openalex.org/W3173193424","https://openalex.org/W4224263489","https://openalex.org/W4225568525","https://openalex.org/W4244655435","https://openalex.org/W4296819410"],"related_works":["https://openalex.org/W2127243424","https://openalex.org/W4390394189","https://openalex.org/W2037504162","https://openalex.org/W2539013788","https://openalex.org/W2792706544","https://openalex.org/W1568451138","https://openalex.org/W2156699640","https://openalex.org/W2045265907","https://openalex.org/W2972997031","https://openalex.org/W34555840"],"abstract_inverted_index":{"BACKGROUND:":[0],"The":[1,22,309],"spectrum":[2],"of":[3,8,17,52,76,153,161,196,211,266,294],"mutations":[4],"in":[5,286,304,322],"a":[6,15,18,42,50,113,118,141,178,182,208,232],"collection":[7],"cancer":[9,224],"genomes":[10],"can":[11,25,328],"be":[12,26,64,329],"described":[13],"by":[14,72,147],"mixture":[16],"few":[19],"mutational":[20,23,36,47,53,59,306],"signatures.":[21,54,154,295],"signatures":[24,37,197,267],"found":[27,330],"using":[28,90],"non-negative":[29],"matrix":[30],"factorization":[31],"(NMF).":[32],"To":[33],"extract":[34],"the":[35,45,58,68,74,80,88,96,103,125,131,151,159,162,194,227,239,263,283,287,291,297,302,305,323],"we":[38,157,186,230,251],"have":[39],"to":[40,63,123,149,235],"assume":[41],"distribution":[43,83,106],"for":[44,87,135,289,311],"observed":[46],"counts":[48,60,97],"and":[49,67,84,101,129,214,222,237,248,316,327],"number":[51,152,195,265,293],"In":[55],"most":[56],"applications,":[57],"are":[61,98,191],"assumed":[62],"Poisson":[65],"distributed,":[66],"rank":[69,89],"is":[70,107,200,258,279,320],"chosen":[71],"comparing":[73],"fit":[75],"several":[77],"models":[78],"with":[79,117,169,181],"same":[81],"underlying":[82],"different":[85],"values":[86],"classical":[91,171],"model":[92,143,172,240,255,269,276,313],"selection":[93,144,173,256,277,314],"procedures.":[94,174],"However,":[95],"often":[99],"overdispersed,":[100],"thus":[102],"Negative":[104,114,317],"Binomial":[105,115,318],"more":[108,259,280],"appropriate.":[109],"RESULTS:":[110],"We":[111,138,175,202,271],"propose":[112],"NMF":[116,319],"patient":[119],"specific":[120],"dispersion":[121],"parameter":[122,136],"capture":[124],"variation":[126],"across":[127],"patients":[128],"derive":[130],"corresponding":[132],"update":[133],"rules":[134],"estimation.":[137],"also":[139,176,272],"introduce":[140],"novel":[142],"procedure":[145,257,278,315],"inspired":[146],"cross-validation":[148],"determine":[150],"Using":[155],"simulations,":[156],"study":[158,180],"influence":[160],"distributional":[163],"assumption":[164],"on":[165,207,215,246],"our":[166,204,244,254,275,312],"method":[167,183],"together":[168],"other":[170],"present":[177],"simulation":[179],"comparison":[184],"where":[185],"show":[187,252,273],"that":[188,253,274],"state-of-the-art":[189],"methods":[190,285],"highly":[192],"overestimating":[193],"when":[198],"overdispersion":[199,303],"present.":[201],"apply":[203],"proposed":[205],"analysis":[206,234,299],"wide":[209],"range":[210],"simulated":[212,247],"data":[213,218,229,250],"two":[216],"real":[217,228,249],"sets":[219],"from":[220],"breast":[221],"prostate":[223],"patients.":[225],"On":[226],"describe":[231],"residual":[233,298],"investigate":[236],"validate":[238],"choice.":[241],"CONCLUSIONS:":[242],"With":[243],"results":[245],"robust":[260],"at":[261,331],"determining":[262],"correct":[264],"under":[268],"misspecification.":[270],"accurate":[281],"than":[282],"available":[284,321],"literature":[288],"finding":[290],"true":[292],"Lastly,":[296],"clearly":[300],"emphasizes":[301],"count":[307],"data.":[308],"code":[310],"R":[324],"package":[325],"SigMoS":[326],"https://github.com/MartaPelizzola/SigMoS":[332],".":[333]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4}],"updated_date":"2026-05-21T09:19:25.381259","created_date":"2025-10-10T00:00:00"}
