Cross-Species Meta-Analysis of Transcriptomic Data in Combination With Supervised Machine Learning Models Identifies the Common Gene Signature of Lactation Process

dc.contributor.authorFarhadian, Mohammad
dc.contributor.authorRafat, Seyed A
dc.contributor.authorHasanpur, Karim
dc.contributor.authorEbrahimi, Mansour
dc.contributor.authorEbrahimie, Esmaeil
dc.date.accessioned2018-09-14T01:28:26Z
dc.date.available2018-09-14T01:28:26Z
dc.date.issued2018-07-12
dc.descriptionNote: This article was submitted to Bioinformatics and Computational Biology, a section of the journal Frontiers in Genetics. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en_US
dc.description.abstractLactation, a physiologically complex process, takes place in mammary gland after parturition. The expression profile of the effective genes in lactation has not comprehensively been elucidated. Herein, meta-analysis, using publicly available microarray data, was conducted identify the differentially expressed genes (DEGs) between pre- and post-peak milk production. Three microarray datasets of Rat, Bos Taurus, and Tammar wallaby were used. Samples related to pre-peak (n = 85) and post-peak (n = 24) milk production were selected. Meta-analysis revealed 31 DEGs across the studied species. Interestingly, 10 genes, including MRPS18B, SF1, UQCRC1, NUCB1, RNF126, ADSL, TNNC1, FIS1, HES5 and THTPA, were not detected in original studies that highlights meta-analysis power in biosignature discovery. Common target and regulator analysis highlighted the high connectivity of CTNNB1, CDD4 and LPL as gene network hubs. As data originally came from three different species, to check the effects of heterogeneous data sources on DEGs, 10 attribute weighting (machine learning) algorithms were applied. Attribute weighting results showed that the type of organism had no or little effect on the selected gene list. Systems biology analysis suggested that these DEGs affect the milk production by improving the immune system performance and mammary cell growth. This is the first study employing both meta-analysis and machine learning approaches for comparative analysis of gene expression pattern of mammary glands in two important time points of lactation process. The finding may pave the way to use of publically available to elucidate the underlying molecular mechanisms of physiologically complex traits such as lactation in mammals.en_US
dc.description.sponsorshipThis study is supported by University of Tabriz.en_US
dc.identifier.citationFarhadian, M., Rafat, S.A., Hasanpur, K., Ebrahimi, M. and Ebrahimie, E., (2018). Cross-Species Meta-Analysis of Transcriptomic Data in Combination With Supervised Machine Learning Models Identifies the Common Gene Signature of Lactation Process. Front. Genet. 9:235. doi: 10.3389/fgene.2018.00235en_US
dc.identifier.doihttps://doi.org/10.3389/fgene.2018.00235en_US
dc.identifier.issn1664-8021
dc.identifier.urihttp://hdl.handle.net/2328/38281
dc.language.isoenen_US
dc.oaire.license.condition.licenseCC-BY
dc.publisherFrontiers Mediaen_US
dc.rightsCopyright © 2018 Farhadian, Rafat, Hasanpur, Ebrahimi and Ebrahimie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en_US
dc.rights.holderCopyright © 2018 Farhadian, Rafat, Hasanpur, Ebrahimi and Ebrahimie.en_US
dc.subjectmilk productionen_US
dc.subjectmeta-analysisen_US
dc.subjectmicroarrayen_US
dc.subjectgene ontologyen_US
dc.subjectgene networken_US
dc.subjectdata miningen_US
dc.titleCross-Species Meta-Analysis of Transcriptomic Data in Combination With Supervised Machine Learning Models Identifies the Common Gene Signature of Lactation Processen_US
dc.typeArticleen
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