An integrative machine learning approach to discovering multi-level molecular mechanisms of obesity using data from monozygotic twin pairs

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Pysyväisosoite

http://hdl.handle.net/10138/325958

Lähdeviite

Kibble , M , Khan , S A , Ammad-ud-din , M , Bollepalli , S , Palviainen , T , Kaprio , J , Pietiläinen , K H & Ollikainen , M 2020 , ' An integrative machine learning approach to discovering multi-level molecular mechanisms of obesity using data from monozygotic twin pairs ' , Royal Society Open Science , vol. 7 , no. 10 , 200872 . https://doi.org/10.1098/rsos.200872

Julkaisun nimi: An integrative machine learning approach to discovering multi-level molecular mechanisms of obesity using data from monozygotic twin pairs
Tekijä: Kibble, Milla; Khan, Suleiman A.; Ammad-ud-din, Muhammad; Bollepalli, Sailalitha; Palviainen, Teemu; Kaprio, Jaakko; Pietiläinen, Kirsi H.; Ollikainen, Miina
Tekijän organisaatio: Epigenetics of Complex Diseases and Traits
Institute for Molecular Medicine Finland
Genetic Epidemiology
Department of Public Health
University of Helsinki
HUS Abdominal Center
Department of Medicine
Clinicum
Research Programs Unit
Helsinki University Hospital Area
Päiväys: 2020-10-21
Kieli: eng
Sivumäärä: 21
Kuuluu julkaisusarjaan: Royal Society Open Science
ISSN: 2054-5703
DOI-tunniste: https://doi.org/10.1098/rsos.200872
URI: http://hdl.handle.net/10138/325958
Tiivistelmä: We combined clinical, cytokine, genomic, methylation and dietary data from 43 young adult monozygotic twin pairs (aged 22-36 years, 53% female), where 25 of the twin pairs were substantially weight discordant (delta body mass index > 3 kg m(-2)). These measurements were originally taken as part of the TwinFat study, a substudy of The Finnish Twin Cohort study. These five large multivariate datasets (comprising 42, 71, 1587, 1605 and 63 variables, respectively) were jointly analysed using an integrative machine learning method called group factor analysis (GFA) to offer new hypotheses into the multi-molecular-level interactions associated with the development of obesity. New potential links between cytokines and weight gain are identified, as well as associations between dietary, inflammatory and epigenetic factors. This encouraging case study aims to enthuse the research community to boldly attempt new machine learning approaches which have the potential to yield novel and unintuitive hypotheses. The source code of the GFA method is publically available as the R package GFA.
Avainsanat: machine learning
big data
obesity
monozygotic twins
BODY-MASS INDEX
ARYL-HYDROCARBON RECEPTOR
SUBCUTANEOUS ADIPOSE-TISSUE
TIME PHYSICAL-ACTIVITY
GENE-EXPRESSION
DNA METHYLATION
INSULIN-RESISTANCE
WIDE ASSOCIATION
GUT MICROBIOME
DRUG RESPONSE
3121 General medicine, internal medicine and other clinical medicine
Vertaisarvioitu: Kyllä
Pääsyrajoitteet: openAccess
Rinnakkaistallennettu versio: publishedVersion


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