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T1  - An integrative machine learning approach to discovering multi-level molecular mechanisms of obesity using data from monozygotic twin pairs 
SN  -  /  
UR  - http://hdl.handle.net/10138/325958 
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A1  - Kibble, Milla; Khan, Suleiman A.; Ammad-ud-din, Muhammad; Bollepalli, Sailalitha; Palviainen, Teemu; Kaprio, Jaakko; Pietiläinen, Kirsi H.; Ollikainen, Miina 
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Y1  - 2020 
LA  - eng 
AB  - 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 inte... 
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KW  - 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 
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