{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T08:58:12Z","timestamp":1767171492518,"version":"build-2238731810"},"reference-count":13,"publisher":"Springer Science and Business Media LLC","issue":"S24","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2019,12,20]],"date-time":"2019-12-20T00:00:00Z","timestamp":1576800000000},"content-version":"vor","delay-in-days":19,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Between June 9\u201311, 2019, the International Conference on Intelligent Biology and Medicine (ICIBM 2019) was held in Columbus, Ohio, USA. The conference included 12 scientific sessions, five tutorials or workshops, one poster session, four keynote talks and four eminent scholar talks that covered a wide range of topics in bioinformatics, medical informatics, systems biology and intelligent computing. Here, we describe 13 high quality research articles selected for publishing in BMC Bioinformatics.<\/jats:p>","DOI":"10.1186\/s12859-019-3240-4","type":"journal-article","created":{"date-parts":[[2019,12,20]],"date-time":"2019-12-20T03:05:23Z","timestamp":1576811123000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["The International Conference on Intelligent Biology and Medicine (ICIBM) 2019: bioinformatics methods and applications for human diseases"],"prefix":"10.1186","volume":"20","author":[{"given":"Zhongming","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yulin","family":"Dai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ewy","family":"Math\u00e9","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lai","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,12,20]]},"reference":[{"key":"3240_CR1","doi-asserted-by":"publisher","unstructured":"Ayed M, Lim H, Xie L. Biological representation of chemicals using latent target interaction profile. BMC Bioinformatics. 2019;20(s24). https:\/\/doi.org\/10.1186\/s12859-019-3241-3.","DOI":"10.1186\/s12859-019-3241-3"},{"key":"3240_CR2","doi-asserted-by":"publisher","unstructured":"Feng X, Wang Z, Li H, Li S. MIRIA: a webserver for statistical, visual and meta-analysis of RNA editing data in mammals. BMC Bioinformatics. 2019;20(s24). https:\/\/doi.org\/10.1186\/s12859-019-3242-2.","DOI":"10.1186\/s12859-019-3242-2."},{"key":"3240_CR3","doi-asserted-by":"publisher","unstructured":"Zhang Y, Wan C, Wang P, Chang W, Huo Y, Chen J, Ma Q, Cao S, Zhang C. M3S: A comprehensive model selection for multi-modal single-cell RNA sequencing data. BMC Bioinformatics. 2019;20(s24). https:\/\/doi.org\/10.1186\/s12859-019-3243-1.","DOI":"10.1186\/s12859-019-3243-1."},{"key":"3240_CR4","doi-asserted-by":"publisher","unstructured":"Cui H, Hu H, Zeng J, Chen T. DeepShape: estimating isoform-level ribosome abundance and distribution with Ribo-seq data. BMC Bioinformatics. 2019;20(s24). https:\/\/doi.org\/10.1186\/s12859-019-3244-0.","DOI":"10.1186\/s12859-019-3244-0."},{"key":"3240_CR5","doi-asserted-by":"publisher","unstructured":"Wu J, Liu Y, Chang T. SigUNet: signal peptide recognition based on semantic segmentation. BMC Bioinformatics. 2019;20(s24). https:\/\/doi.org\/10.1186\/s12859-019-3245-z.","DOI":"10.1186\/s12859-019-3245-z."},{"key":"3240_CR6","doi-asserted-by":"publisher","unstructured":"Liu Y, Xu J, Li S. A unified STR profiling system across multiple species with whole genome sequencing data. BMC Bioinformatics. 2019;20(s24). https:\/\/doi.org\/10.1186\/s12859-019-3246-y.","DOI":"10.1186\/s12859-019-3246-y."},{"key":"3240_CR7","doi-asserted-by":"publisher","unstructured":"Abrams Z, Johnson T, Huang K. Philip Payne, Kevin Coombes. A protocol to evaluate RNA sequencing normalization methods. BMC Bioinformatics. 2019;20(s24). https:\/\/doi.org\/10.1186\/s12859-019-3247-x.","DOI":"10.1186\/s12859-019-3247-x."},{"key":"3240_CR8","doi-asserted-by":"publisher","unstructured":"Yu L, Zhang J, Brock G, Fernandez S. Fully moderated T-statistic in linear modeling of mixed effects for differential expression analysis. BMC Bioinformatics. 2019;20(s24). https:\/\/doi.org\/10.1186\/s12859-019-3248-9.","DOI":"10.1186\/s12859-019-3248-9."},{"key":"3240_CR9","doi-asserted-by":"publisher","unstructured":"Klein J, Sun Z, Staff N. Association between ALS and retroviruses: evidence from bioinformatics analysis. BMC Bioinformatics. 2019;20(s24). https:\/\/doi.org\/10.1186\/s12859-019-3249-8.","DOI":"10.1186\/s12859-019-3249-8."},{"key":"3240_CR10","doi-asserted-by":"publisher","unstructured":"Shah J, Brock G, Gaskins J. BayesMetab: treatment of missing values in Metabolomic studies using a Bayesian modeling approach. BMC Bioinformatics. 2019;20(s24). https:\/\/doi.org\/10.1186\/s12859-019-3250-2.","DOI":"10.1186\/s12859-019-3250-2."},{"key":"3240_CR11","doi-asserted-by":"publisher","unstructured":"Gadepalli VS, Ozer HG, Yilmaz AS, Pietrzak M, Webb A. BISR-RNAseq: an efficient and scalable RNAseq analysis workflow with interactive report generation. BMC Bioinformatics. 2019;20(s24). https:\/\/doi.org\/10.1186\/s12859-019-3251-1.","DOI":"10.1186\/s12859-019-3251-1."},{"key":"3240_CR12","doi-asserted-by":"publisher","unstructured":"Church B, Williams H, Mar J. Investigating Skewness to understand gene expression heterogeneity in large patient cohorts. BMC Bioinformatics. 2019;20(s24). https:\/\/doi.org\/10.1186\/s12859-019-3252-0.","DOI":"10.1186\/s12859-019-3252-0."},{"key":"3240_CR13","doi-asserted-by":"publisher","unstructured":"Eicher T, Patt A, Kautto E, Machiraju R, Mathe E, Zhang Y. Challenges in proteogenomics: a comparison of analysis methods with the case study of the DREAM Proteogenomics sub-challenge. BMC Bioinformatics. 2019;20(s24). https:\/\/doi.org\/10.1186\/s12859-019-3253-z.","DOI":"10.1186\/s12859-019-3253-z."}],"updated-by":[{"DOI":"10.1186\/s12859-020-3487-9","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2020,4,16]],"date-time":"2020-04-16T00:00:00Z","timestamp":1586995200000}}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-019-3240-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12859-019-3240-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-019-3240-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,18]],"date-time":"2020-12-18T19:05:19Z","timestamp":1608318319000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-019-3240-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12]]},"references-count":13,"journal-issue":{"issue":"S24","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["3240"],"URL":"https:\/\/doi.org\/10.1186\/s12859-019-3240-4","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12]]},"assertion":[{"value":"20 December 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 April 2020","order":2,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":3,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"After publication of this supplement article [1], it is requested the grant ID in the Funding section should be corrected from NSF grant IIS-7811367 to NSF grant IIS-1902617. Therefore, the correct \u2018Funding\u2019 section in this article should read: We thank the National Science Foundation (NSF grant IIS-1902617) for the financial support of ICIBM 2019. This article has not received sponsorship for publication.","order":4,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"676"}}