An integrative machine learning approach to discovering multi-level molecular mechanisms of obesity using data from monozygotic twin pairs
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...
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| Format: | Article |
| Language: | English |
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The Royal Society
2020-10-01
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| Series: | Royal Society Open Science |
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| Online Access: | https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.200872 |
| _version_ | 1828890162640191488 |
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| author | Milla Kibble Suleiman A. Khan Muhammad Ammad-ud-din Sailalitha Bollepalli Teemu Palviainen Jaakko Kaprio Kirsi H. Pietiläinen Miina Ollikainen |
| author_facet | Milla Kibble Suleiman A. Khan Muhammad Ammad-ud-din Sailalitha Bollepalli Teemu Palviainen Jaakko Kaprio Kirsi H. Pietiläinen Miina Ollikainen |
| author_sort | Milla Kibble |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T12:54:01Z |
| format | Article |
| id | doaj.art-476425aae6fd400885c9915b23f20e29 |
| institution | Directory Open Access Journal |
| issn | 2054-5703 |
| language | English |
| last_indexed | 2024-12-13T12:54:01Z |
| publishDate | 2020-10-01 |
| publisher | The Royal Society |
| record_format | Article |
| series | Royal Society Open Science |
| spelling | doaj.art-476425aae6fd400885c9915b23f20e292022-12-21T23:45:15ZengThe Royal SocietyRoyal Society Open Science2054-57032020-10-0171010.1098/rsos.200872200872An integrative machine learning approach to discovering multi-level molecular mechanisms of obesity using data from monozygotic twin pairsMilla KibbleSuleiman A. KhanMuhammad Ammad-ud-dinSailalitha BollepalliTeemu PalviainenJaakko KaprioKirsi H. PietiläinenMiina OllikainenWe 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.https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.200872machine learningbig dataobesitymonozygotic twins |
| spellingShingle | Milla Kibble Suleiman A. Khan Muhammad Ammad-ud-din Sailalitha Bollepalli Teemu Palviainen Jaakko Kaprio Kirsi H. Pietiläinen Miina Ollikainen An integrative machine learning approach to discovering multi-level molecular mechanisms of obesity using data from monozygotic twin pairs Royal Society Open Science machine learning big data obesity monozygotic twins |
| title | An integrative machine learning approach to discovering multi-level molecular mechanisms of obesity using data from monozygotic twin pairs |
| title_full | An integrative machine learning approach to discovering multi-level molecular mechanisms of obesity using data from monozygotic twin pairs |
| title_fullStr | An integrative machine learning approach to discovering multi-level molecular mechanisms of obesity using data from monozygotic twin pairs |
| title_full_unstemmed | An integrative machine learning approach to discovering multi-level molecular mechanisms of obesity using data from monozygotic twin pairs |
| title_short | An integrative machine learning approach to discovering multi-level molecular mechanisms of obesity using data from monozygotic twin pairs |
| title_sort | integrative machine learning approach to discovering multi level molecular mechanisms of obesity using data from monozygotic twin pairs |
| topic | machine learning big data obesity monozygotic twins |
| url | https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.200872 |
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