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

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About: Cross-Species Meta-Analysis of Transcriptomic Data in Combination With Supervised Machine Learning Models Identifies the Common Gene Signature of Lactation Process Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Cross-Species Meta-Analysis of Transcriptomic Data in Combination With Supervised Machine Learning Models Identifies the Common Gene Signature of Lactation Process
Creator	Ebrahimi, Mansour Ebrahimie, Esmaeil Farhadian, Mohammad Hasanpur, Karim Poidinger, Michael Rafat, Seyed Caballero, Juan Ferrandez, Jose
Source	PMC
abstract	Lactation, 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.
has issue date	2018-07-12(xsd:dateTime)
bibo:doi	10.3389/fgene.2018.00235
bibo:pmid	30050559
has license	cc-by
sha1sum (hex)	88f92fbe9b0ef301ed99a0f84c88beca964fad97
schema:url	https://doi.org/10.3389/fgene.2018.00235
resource representing a document's title	Cross-Species Meta-Analysis of Transcriptomic Data in Combination With Supervised Machine Learning Models Identifies the Common Gene Signature of Lactation Process
has PubMed Central identifier	PMC6052129
has PubMed identifier	30050559
schema:publication	Front Genet
resource representing a document's body	covid:88f92fbe9b0ef301ed99a0f84c88beca964fad97#body_text
is schema:about of	named entity 'LACTATION' named entity 'PROCESS' named entity 'COMMON' named entity 'AVAILABLE' named entity 'IMPORTANT' named entity 'FIS1' named entity 'EXPRESSED' named entity 'META-ANALYSIS' named entity 'STUDIES' named entity 'BOS TAURUS' named entity 'AFFECT' named entity 'TNNC1' named entity 'MILK PRODUCTION' named entity 'IMMUNE SYSTEM' named entity 'TRANSCRIPTOMIC DATA' named entity 'META-ANALYSIS' named entity 'SPECIES' named entity 'MRPS18B' named entity 'POST' named entity 'CAME' named entity 'DEGS' named entity 'REGULATOR' named entity 'PAVE' named entity 'NUCB1' covid:arg/88f92fbe9b0ef301ed99a0f84c88beca964fad97 named entity 'SELECTED' named entity 'MICROARRAY DATA' named entity 'MAMMALS' named entity 'THESE' named entity 'HETEROGENEOUS' named entity 'APPROACHES' named entity 'STUDY' named entity 'MICROARRAY' named entity 'WEIGHTING' named entity 'CONNECTIVITY' named entity 'TIME' named entity 'GENE LIST' named entity 'EXPRESSION PROFILE' named entity 'ATTRIBUTE' named entity 'MODELS' named entity 'CITATION' named entity 'EFFECT' named entity 'RESULTS' named entity 'EMPLOYING' named entity 'RAT' named entity 'POINTS' named entity 'ORIGINAL' named entity 'GENE SIGNATURE' named entity 'SUPERVISED MACHINE LEARNING' named entity 'MAMMARY GLAND' named entity 'SYSTEMS BIOLOGY ANALYSIS' named entity 'USING' named entity 'AS GENE' named entity 'RELATED' named entity 'INCLUDING' named entity 'DISCOVERY' named entity 'LITTLE' named entity 'PRE' named entity 'DIFFERENT' named entity 'COMPLEX TRAITS' named entity 'COMPLEX' named entity 'GENE EXPRESSION PATTERN' named entity 'ALGORITHMS' named entity 'ANALYSIS' named entity 'PERFORMANCE' named entity 'IMPROVING' named entity 'IDENTIFY' named entity 'COMMON' named entity 'ORGANISM' named entity 'TYPE' named entity 'EFFECTIVE' named entity 'UQCRC1' named entity 'WAY' named entity 'CTNNB1'

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