{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,30]],"date-time":"2025-08-30T17:15:12Z","timestamp":1756574112021,"version":"3.37.3"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"Supplement_2","license":[{"start":{"date-parts":[[2020,12,31]],"date-time":"2020-12-31T00:00:00Z","timestamp":1609372800000},"content-version":"vor","delay-in-days":30,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CNS-1618207"],"award-info":[{"award-number":["CNS-1618207"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["DGE-1632976"],"award-info":[{"award-number":["DGE-1632976"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000923","name":"Silicon Valley Community Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000923","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,12,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Mapping genetic interactions (GIs) can reveal important insights into cellular function and has potential translational applications. There has been great progress in developing high-throughput experimental systems for measuring GIs (e.g. with double knockouts) as well as in defining computational methods for inferring (imputing) unknown interactions. However, existing computational methods for imputation have largely been developed for and applied in baker\u2019s yeast, even as experimental systems have begun to allow measurements in other contexts. Importantly, existing methods face a number of limitations in requiring specific side information and with respect to computational cost. Further, few have addressed how GIs can be imputed when data are scarce.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this article, we address these limitations by presenting a new imputation framework, called Extensible Matrix Factorization (EMF). EMF is a framework of composable models that flexibly exploit cross-species information in the form of GI data across multiple species, and arbitrary side information in the form of kernels (e.g. from protein\u2013protein interaction networks). We perform a rigorous set of experiments on these models in matched GI datasets from baker\u2019s and fission yeast. These include the first such experiments on genome-scale GI datasets in multiple species in the same study. We find that EMF models that exploit side and cross-species information improve imputation, especially in data-scarce settings. Further, we show that EMF outperforms the state-of-the-art deep learning method, even when using strictly less data, and incurs orders of magnitude less computational cost.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability<\/jats:title>\n                  <jats:p>Implementations of models and experiments are available at: https:\/\/github.com\/lrgr\/EMF.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa818","type":"journal-article","created":{"date-parts":[[2020,9,9]],"date-time":"2020-09-09T19:27:03Z","timestamp":1599679623000},"page":"i866-i874","source":"Crossref","is-referenced-by-count":2,"title":["Matrix (factorization) reloaded: flexible methods for imputing genetic interactions with cross-species and side information"],"prefix":"10.1093","volume":"36","author":[{"given":"Jason","family":"Fan","sequence":"first","affiliation":[{"name":"Department of Computer Science and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuan Cindy","family":"Li","sequence":"additional","affiliation":[{"name":"Program in Computational Biology, Bioinformatics, and Genomics, University of Maryland , College Park, MD 20742, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark","family":"Crovella","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Boston University , MA, 02215, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark D M","family":"Leiserson","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2020,12,29]]},"reference":[{"first-page":"265","year":"2016","author":"Abadi","key":"2023062504245330300_btaa818-B1"},{"key":"2023062504245330300_btaa818-B2","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/S0022-2836(05)80360-2","article-title":"Basic local alignment search tool","volume":"215","author":"Altschul","year":"1990","journal-title":"J. Mol. Biol"},{"key":"2023062504245330300_btaa818-B3","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1038\/s41571-018-0055-6","article-title":"Synthetic lethal therapies for cancer: what\u2019s next after PARP inhibitors?","volume":"15","author":"Ashworth","year":"2018","journal-title":"Nat. Rev. Clin. Oncol"},{"key":"2023062504245330300_btaa818-B4","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/S0076-6879(10)70007-0","volume-title":"Guide to Yeast Genetics: Functional Genomics, Proteomics, and Other Systems Analysis.","author":"Baryshnikova","year":"2010"},{"key":"2023062504245330300_btaa818-B5","first-page":"e1006888","article-title":"Predicting synthetic lethal interactions using conserved patterns in protein interaction networks","volume-title":"PLoS Comput. Biol","author":"Benstead-Hume","year":"2019"},{"first-page":"2546","year":"2011","author":"Bergstra","key":"2023062504245330300_btaa818-B6"},{"key":"2023062504245330300_btaa818-B7","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1007\/s10208-009-9045-5","article-title":"Exact matrix completion via convex optimization","volume":"9","author":"Cand\u00e8s","year":"2009","journal-title":"Found. Comput. Math"},{"key":"2023062504245330300_btaa818-B8","doi-asserted-by":"crossref","first-page":"D700","DOI":"10.1093\/nar\/gkr1029","article-title":"Saccharomyces Genome Database: the genomics resource of budding yeast","volume":"40","author":"Cherry","year":"2012","journal-title":"Nucleic Acids Res"},{"key":"2023062504245330300_btaa818-B9","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1038\/nature05649","article-title":"Functional dissection of protein complexes involved in yeast chromosome biology using a genetic interaction map","volume":"446","author":"Collins","year":"2007","journal-title":"Nature"},{"key":"2023062504245330300_btaa818-B10","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1126\/science.1180823","article-title":"The genetic landscape of a cell","volume":"327","author":"Costanzo","year":"2010","journal-title":"Science"},{"key":"2023062504245330300_btaa818-B11","doi-asserted-by":"crossref","first-page":"aaf1420","DOI":"10.1126\/science.aaf1420","article-title":"A global genetic interaction network maps a wiring diagram of cellular function","volume":"353","author":"Costanzo","year":"2016","journal-title":"Science"},{"key":"2023062504245330300_btaa818-B12","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.cell.2019.01.033","article-title":"Global genetic networks and the genotype-to-phenotype relationship","volume":"177","author":"Costanzo","year":"2019","journal-title":"Cell"},{"key":"2023062504245330300_btaa818-B13","doi-asserted-by":"crossref","first-page":"1853","DOI":"10.1016\/j.cell.2016.11.038","article-title":"Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens","volume":"167","author":"Dixit","year":"2016","journal-title":"Cell"},{"key":"2023062504245330300_btaa818-B14","doi-asserted-by":"crossref","first-page":"e51","DOI":"10.1093\/nar\/gkz132","article-title":"Functional protein representations from biological networks enable diverse cross-species inference","volume":"47","author":"Fan","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2023062504245330300_btaa818-B15","first-page":"2211","article-title":"Multiple kernel learning algorithms","volume":"12","author":"G\u00f6nen","year":"2011","journal-title":"J. Mach. Learn. Res"},{"key":"2023062504245330300_btaa818-B16","doi-asserted-by":"crossref","first-page":"aad6253","DOI":"10.1126\/science.aad6253","article-title":"Design and synthesis of a minimal bacterial genome","volume":"351","author":"Hutchison","year":"2016","journal-title":"Science"},{"key":"2023062504245330300_btaa818-B17","doi-asserted-by":"crossref","first-page":"e1004506","DOI":"10.1371\/journal.pcbi.1004506","article-title":"Connectivity homology enables inter-species network models of synthetic lethality","volume":"11","author":"Jacunski","year":"2015","journal-title":"PLoS Comput. Biol"},{"year":"2017","author":"Kingma","key":"2023062504245330300_btaa818-B18"},{"key":"2023062504245330300_btaa818-B19","doi-asserted-by":"crossref","first-page":"R57","DOI":"10.1186\/gb-2012-13-7-r57","article-title":"Conserved rules govern genetic interaction degree across species","volume":"13","author":"Koch","year":"2012","journal-title":"Genome Biol"},{"key":"2023062504245330300_btaa818-B20","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MC.2009.263","article-title":"Matrix factorization techniques for recommender systems","volume":"42","author":"Koren","year":"2009","journal-title":"Computer"},{"key":"2023062504245330300_btaa818-B21","doi-asserted-by":"crossref","first-page":"2520","DOI":"10.1093\/bioinformatics\/bts480","article-title":"Snakemake - a scalable bioinformatics workflow engine","volume":"28","author":"K\u00f6ster","year":"2012","journal-title":"Bioinformatics"},{"key":"2023062504245330300_btaa818-B22","doi-asserted-by":"crossref","first-page":"eaao1729","DOI":"10.1126\/science.aao1729","article-title":"Systematic analysis of complex genetic interactions","volume":"360","author":"Kuzmin","year":"2018","journal-title":"Science"},{"key":"2023062504245330300_btaa818-B23","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1038\/44565","article-title":"Learning the parts of objects by non-negative matrix factorization","volume":"401","author":"Lee","year":"1999","journal-title":"Nature"},{"key":"2023062504245330300_btaa818-B24","doi-asserted-by":"crossref","first-page":"2546","DOI":"10.1038\/s41467-018-04647-1","article-title":"Harnessing synthetic lethality to predict the response to cancer treatment","volume":"9","author":"Lee","year":"2018","journal-title":"Nat. Commun"},{"key":"2023062504245330300_btaa818-B25","first-page":"564","volume-title":"Biocomputing","author":"Leslie","year":"2001"},{"first-page":"556","year":"2003","author":"Liben-Nowell","key":"2023062504245330300_btaa818-B26"},{"key":"2023062504245330300_btaa818-B27","doi-asserted-by":"crossref","first-page":"D821","DOI":"10.1093\/nar\/gky961","article-title":"PomBase 2018: user-driven reimplementation of the fission yeast database provides rapid and intuitive access to diverse, interconnected information","volume":"47","author":"Lock","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2023062504245330300_btaa818-B28","doi-asserted-by":"crossref","first-page":"1150","DOI":"10.1016\/j.physa.2010.11.027","article-title":"Link prediction in complex networks: a survey","volume":"390","author":"L\u00fc","year":"2011","journal-title":"Physica A"},{"key":"2023062504245330300_btaa818-B29","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1038\/nmeth.4627","article-title":"Using deep learning to model the hierarchical structure and function of a cell","volume":"15","author":"Ma","year":"2018","journal-title":"Nat. Methods"},{"key":"2023062504245330300_btaa818-B30","doi-asserted-by":"crossref","first-page":"D529","DOI":"10.1093\/nar\/gky1079","article-title":"The BioGRID interaction database: 2019 update","volume":"47","author":"Oughtred","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2023062504245330300_btaa818-B31","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1186\/1471-2105-9-426","article-title":"Mining protein networks for synthetic genetic interactions","volume":"9","author":"Paladugu","year":"2008","journal-title":"BMC Bioinformatics"},{"key":"2023062504245330300_btaa818-B32","doi-asserted-by":"crossref","first-page":"e1000928","DOI":"10.1371\/journal.pcbi.1000928","article-title":"An integrative multi-network and multi-classifier approach to predict genetic interactions","volume":"6","author":"Pandey","year":"2010","journal-title":"PLoS Comput. Biol"},{"key":"2023062504245330300_btaa818-B33","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.cels.2018.06.010","article-title":"Quantitative yeast genetic interaction profiling of bacterial effector proteins uncovers a role for the human retromer in salmonella infection","volume":"7","author":"Patrick","year":"2018","journal-title":"Cell Syst"},{"year":"2019","author":"Rendle","key":"2023062504245330300_btaa818-B34"},{"key":"2023062504245330300_btaa818-B35","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1126\/science.1162609","article-title":"Conservation and rewiring of functional modules revealed by an epistasis map in fission yeast","volume":"322","author":"Roguev","year":"2008","journal-title":"Science"},{"key":"2023062504245330300_btaa818-B36","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1016\/j.molcel.2012.05.028","article-title":"Hierarchical modularity and the evolution of genetic interactomes across species","volume":"46","author":"Ryan","year":"2012","journal-title":"Mol. Cell"},{"year":"2008","author":"Salakhutdinov","key":"2023062504245330300_btaa818-B37"},{"key":"2023062504245330300_btaa818-B38","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1016\/j.cell.2005.08.031","article-title":"Exploration of the function and organization of the yeast early secretory pathway through an epistatic miniarray profile","volume":"123","author":"Schuldiner","year":"2005","journal-title":"Cell"},{"key":"2023062504245330300_btaa818-B39","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1016\/j.tig.2018.07.003","article-title":"Enter the matrix: factorization uncovers knowledge from omics","volume":"34","author":"Stein-O\u2019Brien","year":"2018","journal-title":"Trends Genet"},{"key":"2023062504245330300_btaa818-B40","first-page":"D330","article-title":"The Gene Ontology Resource: 20 years and still GOing strong","volume":"47","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2023062504245330300_btaa818-B41","doi-asserted-by":"crossref","first-page":"R140","DOI":"10.1186\/gb-2009-10-12-r140","article-title":"Towards accurate imputation of quantitative genetic interactions","volume":"10","author":"Ulitsky","year":"2009","journal-title":"Genome Biol"},{"key":"2023062504245330300_btaa818-B42","doi-asserted-by":"crossref","first-page":"15682","DOI":"10.1073\/pnas.0406614101","article-title":"Combining biological networks to predict genetic interactions","volume":"101","author":"Wong","year":"2004","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023062504245330300_btaa818-B43","doi-asserted-by":"crossref","DOI":"10.4137\/CIN.S14026","article-title":"In silico prediction of synthetic lethality by meta-analysis of genetic interactions, functions, and pathways in yeast and human cancer","volume":"13s3","author":"Wu","year":"2014","journal-title":"Cancer Informatics"},{"key":"2023062504245330300_btaa818-B44","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.cels.2016.02.003","article-title":"Translation of genotype to phenotype by a hierarchy of cell subsystems","volume":"2","author":"Yu","year":"2016","journal-title":"Cell Syst"},{"first-page":"403","year":"2012","author":"Zhou","key":"2023062504245330300_btaa818-B45"},{"key":"2023062504245330300_btaa818-B46","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1089\/cmb.2014.0158","article-title":"Data imputation in epistatic maps by network-guided matrix completion","volume":"22","author":"Zitnik","year":"2015","journal-title":"J. Comput. Biol"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/36\/Supplement_2\/i866\/50693451\/btaa818.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/36\/Supplement_2\/i866\/50693451\/btaa818.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,25]],"date-time":"2023-06-25T04:26:28Z","timestamp":1687667188000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/36\/Supplement_2\/i866\/6055925"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12]]},"references-count":46,"journal-issue":{"issue":"Supplement_2","published-print":{"date-parts":[[2020,12,30]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btaa818","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"type":"print","value":"1367-4803"},{"type":"electronic","value":"1367-4811"}],"subject":[],"published-other":{"date-parts":[[2020,12]]},"published":{"date-parts":[[2020,12]]}}}