{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:00:24Z","timestamp":1777701624609,"version":"3.51.4"},"reference-count":12,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2020,1,31]],"date-time":"2020-01-31T00:00:00Z","timestamp":1580428800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000092","name":"National Library of Medicine","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000092","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000092","name":"NLM","doi-asserted-by":"publisher","award":["R01LM011986"],"award-info":[{"award-number":["R01LM011986"]}],"id":[{"id":"10.13039\/100000092","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"NIGMS","doi-asserted-by":"publisher","award":["R01GM122845"],"award-info":[{"award-number":["R01GM122845"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Institute on Aging of the National Institute of Health"},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01AD057555"],"award-info":[{"award-number":["R01AD057555"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CUNY High Performance Computing Center"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>LINCS L1000 dataset contains numerous cellular expression data induced by large sets of perturbagens. Although it provides invaluable resources for drug discovery as well as understanding of disease mechanisms, the existing peak deconvolution algorithms cannot recover the accurate expression level of genes in many cases, inducing severe noise in the dataset and limiting its applications in biomedical studies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Here, we present a novel Bayesian-based peak deconvolution algorithm that gives unbiased likelihood estimations for peak locations and characterize the peaks with probability based z-scores. Based on the above algorithm, we build a pipeline to process raw data from L1000 assay into signatures that represent the features of perturbagen. The performance of the proposed pipeline is evaluated using similarity between the signatures of bio-replicates and the drugs with shared targets, and the results show that signatures derived from our pipeline gives a substantially more reliable and informative representation for perturbagens than existing methods. Thus, the new pipeline may significantly boost the performance of L1000 data in the downstream applications such as drug repurposing, disease modeling and gene function prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The code and the precomputed data for LINCS L1000 Phase II (GSE 70138) are available at https:\/\/github.com\/njpipeorgan\/L1000-bayesian.<\/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\/btaa064","type":"journal-article","created":{"date-parts":[[2020,1,24]],"date-time":"2020-01-24T07:11:10Z","timestamp":1579849870000},"page":"2787-2795","source":"Crossref","is-referenced-by-count":26,"title":["A Bayesian approach to accurate and robust signature detection on LINCS L1000 data"],"prefix":"10.1093","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9692-1290","authenticated-orcid":false,"given":"Yue","family":"Qiu","sequence":"first","affiliation":[{"name":"Ph.D. Program in Biology , The Graduate Center, The City University of New York, New York, NY 10016, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1040-2639","authenticated-orcid":false,"given":"Tianhuan","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Astronomy , Columbia University, New York, NY 10027, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8420-4750","authenticated-orcid":false,"given":"Hansaim","family":"Lim","sequence":"additional","affiliation":[{"name":"Ph.D. Program in Biochemistry"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Xie","sequence":"additional","affiliation":[{"name":"Ph.D. Program in Biology , The Graduate Center, The City University of New York, New York, NY 10016, USA"},{"name":"Ph.D. Program in Biochemistry"},{"name":"Ph.D. Program in Computer Science , The Graduate Center, The City University of New York, New York, NY 10016, USA"},{"name":"Department of Computer Science , Hunter College, The City University of New York, New York, NY 10016, USA"},{"name":"Helen and Robert Appel Alzheimer\u2019s Disease Research Institute , Feil Family Brain & Mind Research Institute, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2020,1,31]]},"reference":[{"key":"2023013110303172900_btaa064-B1","doi-asserted-by":"crossref","first-page":"W449","DOI":"10.1093\/nar\/gku476","article-title":"LINCS canvas browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures","volume":"42","author":"Duan","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2023013110303172900_btaa064-B2","doi-asserted-by":"crossref","first-page":"16015","DOI":"10.1038\/npjsba.2016.15","article-title":"L1000CDS2: LINCS L1000 characteristic direction signatures search engine","volume":"2","author":"Duan","year":"2016","journal-title":"NPJ Syst. Biol. Appl"},{"key":"2023013110303172900_btaa064-B3","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1093\/bioinformatics\/bty784","article-title":"The GCTx format and cmap{Py, R, M, J} packages: resources for optimized storage and integrated traversal of annotated dense matrices","volume":"35","author":"Enache","year":"2018","journal-title":"Bioinformatics"},{"key":"2023013110303172900_btaa064-B4","doi-asserted-by":"crossref","first-page":"e1005335","DOI":"10.1371\/journal.pcbi.1005335","article-title":"Representing high throughput expression profiles via perturbation barcodes reveals compound targets","volume":"13","author":"Filzen","year":"2017","journal-title":"PLoS Comput. Biol"},{"key":"2023013110303172900_btaa064-B5","author":"Jin","year":"2015"},{"key":"2023013110303172900_btaa064-B6","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.cels.2017.11.001","article-title":"The library of integrated network-based cellular signatures nih program: system-level cataloging of human cells response to perturbations","volume":"6","author":"Keenan","year":"2018","journal-title":"Cell Syst"},{"key":"2023013110303172900_btaa064-B7","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1186\/s12859-017-1767-9","article-title":"l1kdeconv: an R package for peak calling analysis with LINCS L1000 data","volume":"18","author":"Li","year":"2017","journal-title":"BMC Bioinformatics"},{"key":"2023013110303172900_btaa064-B8","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1039\/C4MB00677A","article-title":"Compound signature detection on LINCS L1000 big data","volume":"11","author":"Liu","year":"2015","journal-title":"Mol. 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