FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4316362707> ?p ?o ?g. }

• W4316362707 abstract "Abstract Age and sex are historically understudied factors in biomedical studies even though many complex traits and diseases vary by these factors in their incidence and presentation. As a result, there are massive gaps in our understanding of genes and molecular mechanisms that underlie sex- and age-associated physiology and disease. Hundreds of thousands of publicly-available human transcriptomes capturing gene expression profiles of tissues across the body and subject to various biomedical and clinical factors present an invaluable, yet untapped, opportunity for bridging these gaps. Here, we present a computational framework that leverages these data to infer genome-wide molecular signatures specific to sex and age groups. As the vast majority of these profiles lack age and sex labels, the core idea of our framework is to use the measured expression data to predict missing age/sex metadata and derive the signatures from the predictive models. We first curated ∼30,000 primary samples associated with age and sex information and profiled using microarray and RNA-seq. Then, we used this dataset to infer sex-biased genes within eleven age groups along the human lifespan and then trained machine learning (ML) models to predict these age groups from gene expression values separately within females and males. Specifically, we trained one-vs-rest logistic regression classifiers with elastic-net regularization to classify transcriptomes into age groups. Dataset-level cross validation shows that these ML classifiers are able to discriminate between age groups in a biologically meaningful way in each sex across technologies. Further, these predictive models capture sex-stratified age-group ‘gene signatures’, i.e., the strength and the direction of importance of genes across the genome for each age group in each sex. Enrichment analysis of these gene signatures with prior gene annotations helped in identifying age- and sex-associated multi-tissue and pan-body molecular phenomena (e.g., general immune response, inflammation, metabolism, hormone response). Overall, we have presented a path for effectively leveraging massive public omics data collections to investigate the molecular basis of age- and sex-differences in physiology and disease." @default.
• W4316362707 created "2023-01-16" @default.
• W4316362707 creator A5041275039 @default.
• W4316362707 creator A5053907733 @default.
• W4316362707 date "2023-01-15" @default.
• W4316362707 modified "2023-09-29" @default.
• W4316362707 title "Leveraging public transcriptome data with machine learning to infer pan-body age- and sex-specific molecular phenomena" @default.
• W4316362707 cites W1487018400 @default.
• W4316362707 cites W1816176398 @default.
• W4316362707 cites W1964904043 @default.
• W4316362707 cites W1965079305 @default.
• W4316362707 cites W1973916547 @default.
• W4316362707 cites W1981409633 @default.
• W4316362707 cites W2005543791 @default.
• W4316362707 cites W2010798628 @default.
• W4316362707 cites W2018838463 @default.
• W4316362707 cites W2020541351 @default.
• W4316362707 cites W2027431314 @default.
• W4316362707 cites W2036091970 @default.
• W4316362707 cites W2052204012 @default.
• W4316362707 cites W2052572824 @default.
• W4316362707 cites W2054502486 @default.
• W4316362707 cites W2073640336 @default.
• W4316362707 cites W2088963243 @default.
• W4316362707 cites W2103017472 @default.
• W4316362707 cites W2108700752 @default.
• W4316362707 cites W2113001012 @default.
• W4316362707 cites W2116041602 @default.
• W4316362707 cites W2118258530 @default.
• W4316362707 cites W2121091193 @default.
• W4316362707 cites W2135827569 @default.
• W4316362707 cites W2162212747 @default.
• W4316362707 cites W2168102484 @default.
• W4316362707 cites W2170133058 @default.
• W4316362707 cites W2173367702 @default.
• W4316362707 cites W2484866312 @default.
• W4316362707 cites W2508904198 @default.
• W4316362707 cites W2518536270 @default.
• W4316362707 cites W2529891619 @default.
• W4316362707 cites W2559482520 @default.
• W4316362707 cites W2564049149 @default.
• W4316362707 cites W2586476886 @default.
• W4316362707 cites W2592811885 @default.
• W4316362707 cites W2607978513 @default.
• W4316362707 cites W2624988670 @default.
• W4316362707 cites W2691619554 @default.
• W4316362707 cites W2796533912 @default.
• W4316362707 cites W2898321887 @default.
• W4316362707 cites W2898999052 @default.
• W4316362707 cites W2905232298 @default.
• W4316362707 cites W2906199739 @default.
• W4316362707 cites W2924222341 @default.
• W4316362707 cites W2949612967 @default.
• W4316362707 cites W2954492537 @default.
• W4316362707 cites W2960123537 @default.
• W4316362707 cites W2999304916 @default.
• W4316362707 cites W3016853930 @default.
• W4316362707 cites W3037120109 @default.
• W4316362707 cites W3041664880 @default.
• W4316362707 cites W3045156259 @default.
• W4316362707 cites W3047170019 @default.
• W4316362707 cites W3084331777 @default.
• W4316362707 cites W3088977951 @default.
• W4316362707 cites W3096157475 @default.
• W4316362707 cites W3114302742 @default.
• W4316362707 cites W3137361627 @default.
• W4316362707 cites W3140462654 @default.
• W4316362707 cites W3160304067 @default.
• W4316362707 cites W3207510706 @default.
• W4316362707 cites W4212919921 @default.
• W4316362707 cites W4221065117 @default.
• W4316362707 cites W4281382008 @default.
• W4316362707 cites W4286716053 @default.
• W4316362707 cites W4306871112 @default.
• W4316362707 cites W817098076 @default.
• W4316362707 doi "https://doi.org/10.1101/2023.01.12.523796" @default.
• W4316362707 hasPublicationYear "2023" @default.
• W4316362707 type Work @default.
• W4316362707 citedByCount "0" @default.
• W4316362707 crossrefType "posted-content" @default.
• W4316362707 hasAuthorship W4316362707A5041275039 @default.
• W4316362707 hasAuthorship W4316362707A5053907733 @default.
• W4316362707 hasBestOaLocation W43163627071 @default.
• W4316362707 hasConcept C104317684 @default.
• W4316362707 hasConcept C111919701 @default.
• W4316362707 hasConcept C119857082 @default.
• W4316362707 hasConcept C150194340 @default.
• W4316362707 hasConcept C151956035 @default.
• W4316362707 hasConcept C154945302 @default.
• W4316362707 hasConcept C162317418 @default.
• W4316362707 hasConcept C41008148 @default.
• W4316362707 hasConcept C45804977 @default.
• W4316362707 hasConcept C54355233 @default.
• W4316362707 hasConcept C70721500 @default.
• W4316362707 hasConcept C86803240 @default.
• W4316362707 hasConcept C93518851 @default.
• W4316362707 hasConceptScore W4316362707C104317684 @default.
• W4316362707 hasConceptScore W4316362707C111919701 @default.
• W4316362707 hasConceptScore W4316362707C119857082 @default.
• W4316362707 hasConceptScore W4316362707C150194340 @default.

• next