Loading…
Exploring the correlation between DNA methylation and biological age using an interpretable machine learning framework
DNA methylation plays a significant role in regulating transcription and exhibits a systematic change with age. These changes can be used to predict an individual’s age. First, to identify methylation sites associated with biological age; second, to construct a biological age prediction model and pr...
Saved in:
| Published in: | Scientific reports 2024-10, Vol.14 (1), p.24208-13, Article 24208 |
|---|---|
| Main Authors: | , , , , , , , |
| Format: | Article |
| Language: | English |
| Subjects: | |
| Citations: | Items that this one cites |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | |
|---|---|
| cites | cdi_FETCH-LOGICAL-c422t-85e911dea84c5a11da588745d745293d5ba8a62701115cb64ab8e759a7fac6a23 |
| container_end_page | 13 |
| container_issue | 1 |
| container_start_page | 24208 |
| container_title | Scientific reports |
| container_volume | 14 |
| creator | Zhou, Sheng Chen, Jing Wei, Shanshan Zhou, Chengxing Wang, Die Yan, Xiaofan He, Xun Yan, Pengcheng |
| description | DNA methylation plays a significant role in regulating transcription and exhibits a systematic change with age. These changes can be used to predict an individual’s age. First, to identify methylation sites associated with biological age; second, to construct a biological age prediction model and preliminarily explore the biological significance of methylation-associated genes using machine learning. A biological age prediction model was constructed using human methylation data through data preprocessing, feature selection procedures, statistical analysis, and machine learning techniques. Subsequently, 15 methylation data sets were subjected to in-depth analysis using SHAP, GO enrichment, and KEGG analysis. XGBoost, LightGBM, and CatBoost identified 15 groups of methylation sites associated with biological age. The cg23995914 locus was identified as the most significant contributor to predicting biological age by calculating SHAP values. Furthermore, GO enrichment and KEGG analyses were employed to initially explore the methylated loci’s biological significance. |
| doi_str_mv | 10.1038/s41598-024-75586-9 |
| format | article |
| fullrecord | <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_9357a049144045f0891a3ccb854302f4</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_9357a049144045f0891a3ccb854302f4</doaj_id><sourcerecordid>3116761136</sourcerecordid><originalsourceid>FETCH-LOGICAL-c422t-85e911dea84c5a11da588745d745293d5ba8a62701115cb64ab8e759a7fac6a23</originalsourceid><addsrcrecordid>eNp9kk1v1DAQhiMEotXSP8ABWeLCJeDPxD6hqhRaqYILnK2JM9n1ktiLnW3pv8fb3ZaWA5ZGHo1fP_aM3qp6zeh7RoX-kCVTRteUy7pVSje1eVYdcypVzQXnzx_lR9VJzmtaluJGMvOyOhJG0ka3zXF1ff57M8bkw5LMKyQupoQjzD4G0uF8gxjIp6-nZMJ5dXuoQ-hJ5-MYl97BSGCJZJt3AAjEhxnTJuEM3YhkArfyAcmIkMJOMSSY8Camn6-qFwOMGU8O-6L68fn8-9lFffXty-XZ6VXtJOdzrRUaxnoELZ2CkoHSupWqL8GN6FUHGhreUsaYcl0jodPYKgPtAK4BLhbV5Z7bR1jbTfITpFsbwdu7QkxLC2n2bkRrhGqBSsOkLJMbqDYMhHOdVlJQPsjC-rhnbbbdhL3DMCcYn0CfngS_sst4bRmTuoBVIbw7EFL8tcU828lnh-MIAeM2W8FYS1uhSyyqt_9I13GbQpnVTtW0DWOiKSq-V7kUc044PPyGUbuzid3bxBab2DublDYX1ZvHfTxcuTdFEYi9IG92xsD09-3_YP8AnmTI7Q</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3116761136</pqid></control><display><type>article</type><title>Exploring the correlation between DNA methylation and biological age using an interpretable machine learning framework</title><source>Full-Text Journals in Chemistry (Open access)</source><source>Publicly Available Content (ProQuest)</source><source>PubMed Central</source><source>Springer Nature - nature.com Journals - Fully Open Access</source><creator>Zhou, Sheng ; Chen, Jing ; Wei, Shanshan ; Zhou, Chengxing ; Wang, Die ; Yan, Xiaofan ; He, Xun ; Yan, Pengcheng</creator><creatorcontrib>Zhou, Sheng ; Chen, Jing ; Wei, Shanshan ; Zhou, Chengxing ; Wang, Die ; Yan, Xiaofan ; He, Xun ; Yan, Pengcheng</creatorcontrib><description>DNA methylation plays a significant role in regulating transcription and exhibits a systematic change with age. These changes can be used to predict an individual’s age. First, to identify methylation sites associated with biological age; second, to construct a biological age prediction model and preliminarily explore the biological significance of methylation-associated genes using machine learning. A biological age prediction model was constructed using human methylation data through data preprocessing, feature selection procedures, statistical analysis, and machine learning techniques. Subsequently, 15 methylation data sets were subjected to in-depth analysis using SHAP, GO enrichment, and KEGG analysis. XGBoost, LightGBM, and CatBoost identified 15 groups of methylation sites associated with biological age. The cg23995914 locus was identified as the most significant contributor to predicting biological age by calculating SHAP values. Furthermore, GO enrichment and KEGG analyses were employed to initially explore the methylated loci’s biological significance.</description><identifier>ISSN: 2045-2322</identifier><identifier>EISSN: 2045-2322</identifier><identifier>DOI: 10.1038/s41598-024-75586-9</identifier><identifier>PMID: 39406876</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>631/114/1305 ; 631/208/176/1988 ; Age ; Aging ; Aging - genetics ; Biological age ; Biomarkers ; CpG Islands ; Datasets ; Deoxyribonucleic acid ; DNA ; DNA Methylation ; Epigenetics ; Experimental methods ; Gender ; Gene expression ; Gene loci ; Genomes ; GO enrichment analysis ; Health care ; Humanities and Social Sciences ; Humans ; Interpretable machine learning ; Learning algorithms ; Machine Learning ; Male ; Medicine ; multidisciplinary ; Physiology ; Prediction models ; Research methodology ; Science ; Science (multidisciplinary) ; Shapley Additive exPlanations ; Statistical analysis ; Statistical models ; XGBoost</subject><ispartof>Scientific reports, 2024-10, Vol.14 (1), p.24208-13, Article 24208</ispartof><rights>The Author(s) 2024</rights><rights>2024. The Author(s).</rights><rights>The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>The Author(s) 2024 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c422t-85e911dea84c5a11da588745d745293d5ba8a62701115cb64ab8e759a7fac6a23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/3116761136/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/3116761136?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,74998</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39406876$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhou, Sheng</creatorcontrib><creatorcontrib>Chen, Jing</creatorcontrib><creatorcontrib>Wei, Shanshan</creatorcontrib><creatorcontrib>Zhou, Chengxing</creatorcontrib><creatorcontrib>Wang, Die</creatorcontrib><creatorcontrib>Yan, Xiaofan</creatorcontrib><creatorcontrib>He, Xun</creatorcontrib><creatorcontrib>Yan, Pengcheng</creatorcontrib><title>Exploring the correlation between DNA methylation and biological age using an interpretable machine learning framework</title><title>Scientific reports</title><addtitle>Sci Rep</addtitle><addtitle>Sci Rep</addtitle><description>DNA methylation plays a significant role in regulating transcription and exhibits a systematic change with age. These changes can be used to predict an individual’s age. First, to identify methylation sites associated with biological age; second, to construct a biological age prediction model and preliminarily explore the biological significance of methylation-associated genes using machine learning. A biological age prediction model was constructed using human methylation data through data preprocessing, feature selection procedures, statistical analysis, and machine learning techniques. Subsequently, 15 methylation data sets were subjected to in-depth analysis using SHAP, GO enrichment, and KEGG analysis. XGBoost, LightGBM, and CatBoost identified 15 groups of methylation sites associated with biological age. The cg23995914 locus was identified as the most significant contributor to predicting biological age by calculating SHAP values. Furthermore, GO enrichment and KEGG analyses were employed to initially explore the methylated loci’s biological significance.</description><subject>631/114/1305</subject><subject>631/208/176/1988</subject><subject>Age</subject><subject>Aging</subject><subject>Aging - genetics</subject><subject>Biological age</subject><subject>Biomarkers</subject><subject>CpG Islands</subject><subject>Datasets</subject><subject>Deoxyribonucleic acid</subject><subject>DNA</subject><subject>DNA Methylation</subject><subject>Epigenetics</subject><subject>Experimental methods</subject><subject>Gender</subject><subject>Gene expression</subject><subject>Gene loci</subject><subject>Genomes</subject><subject>GO enrichment analysis</subject><subject>Health care</subject><subject>Humanities and Social Sciences</subject><subject>Humans</subject><subject>Interpretable machine learning</subject><subject>Learning algorithms</subject><subject>Machine Learning</subject><subject>Male</subject><subject>Medicine</subject><subject>multidisciplinary</subject><subject>Physiology</subject><subject>Prediction models</subject><subject>Research methodology</subject><subject>Science</subject><subject>Science (multidisciplinary)</subject><subject>Shapley Additive exPlanations</subject><subject>Statistical analysis</subject><subject>Statistical models</subject><subject>XGBoost</subject><issn>2045-2322</issn><issn>2045-2322</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNp9kk1v1DAQhiMEotXSP8ABWeLCJeDPxD6hqhRaqYILnK2JM9n1ktiLnW3pv8fb3ZaWA5ZGHo1fP_aM3qp6zeh7RoX-kCVTRteUy7pVSje1eVYdcypVzQXnzx_lR9VJzmtaluJGMvOyOhJG0ka3zXF1ff57M8bkw5LMKyQupoQjzD4G0uF8gxjIp6-nZMJ5dXuoQ-hJ5-MYl97BSGCJZJt3AAjEhxnTJuEM3YhkArfyAcmIkMJOMSSY8Camn6-qFwOMGU8O-6L68fn8-9lFffXty-XZ6VXtJOdzrRUaxnoELZ2CkoHSupWqL8GN6FUHGhreUsaYcl0jodPYKgPtAK4BLhbV5Z7bR1jbTfITpFsbwdu7QkxLC2n2bkRrhGqBSsOkLJMbqDYMhHOdVlJQPsjC-rhnbbbdhL3DMCcYn0CfngS_sst4bRmTuoBVIbw7EFL8tcU828lnh-MIAeM2W8FYS1uhSyyqt_9I13GbQpnVTtW0DWOiKSq-V7kUc044PPyGUbuzid3bxBab2DublDYX1ZvHfTxcuTdFEYi9IG92xsD09-3_YP8AnmTI7Q</recordid><startdate>20241015</startdate><enddate>20241015</enddate><creator>Zhou, Sheng</creator><creator>Chen, Jing</creator><creator>Wei, Shanshan</creator><creator>Zhou, Chengxing</creator><creator>Wang, Die</creator><creator>Yan, Xiaofan</creator><creator>He, Xun</creator><creator>Yan, Pengcheng</creator><general>Nature Publishing Group UK</general><general>Nature Publishing Group</general><general>Nature Portfolio</general><scope>C6C</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88A</scope><scope>88E</scope><scope>88I</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20241015</creationdate><title>Exploring the correlation between DNA methylation and biological age using an interpretable machine learning framework</title><author>Zhou, Sheng ; Chen, Jing ; Wei, Shanshan ; Zhou, Chengxing ; Wang, Die ; Yan, Xiaofan ; He, Xun ; Yan, Pengcheng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c422t-85e911dea84c5a11da588745d745293d5ba8a62701115cb64ab8e759a7fac6a23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>631/114/1305</topic><topic>631/208/176/1988</topic><topic>Age</topic><topic>Aging</topic><topic>Aging - genetics</topic><topic>Biological age</topic><topic>Biomarkers</topic><topic>CpG Islands</topic><topic>Datasets</topic><topic>Deoxyribonucleic acid</topic><topic>DNA</topic><topic>DNA Methylation</topic><topic>Epigenetics</topic><topic>Experimental methods</topic><topic>Gender</topic><topic>Gene expression</topic><topic>Gene loci</topic><topic>Genomes</topic><topic>GO enrichment analysis</topic><topic>Health care</topic><topic>Humanities and Social Sciences</topic><topic>Humans</topic><topic>Interpretable machine learning</topic><topic>Learning algorithms</topic><topic>Machine Learning</topic><topic>Male</topic><topic>Medicine</topic><topic>multidisciplinary</topic><topic>Physiology</topic><topic>Prediction models</topic><topic>Research methodology</topic><topic>Science</topic><topic>Science (multidisciplinary)</topic><topic>Shapley Additive exPlanations</topic><topic>Statistical analysis</topic><topic>Statistical models</topic><topic>XGBoost</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhou, Sheng</creatorcontrib><creatorcontrib>Chen, Jing</creatorcontrib><creatorcontrib>Wei, Shanshan</creatorcontrib><creatorcontrib>Zhou, Chengxing</creatorcontrib><creatorcontrib>Wang, Die</creatorcontrib><creatorcontrib>Yan, Xiaofan</creatorcontrib><creatorcontrib>He, Xun</creatorcontrib><creatorcontrib>Yan, Pengcheng</creatorcontrib><collection>SpringerOpen</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Biology Database (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biological Sciences</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>ProQuest Science Journals</collection><collection>Biological Science Database</collection><collection>Publicly Available Content (ProQuest)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>Directory of Open Access Journals</collection><jtitle>Scientific reports</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhou, Sheng</au><au>Chen, Jing</au><au>Wei, Shanshan</au><au>Zhou, Chengxing</au><au>Wang, Die</au><au>Yan, Xiaofan</au><au>He, Xun</au><au>Yan, Pengcheng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Exploring the correlation between DNA methylation and biological age using an interpretable machine learning framework</atitle><jtitle>Scientific reports</jtitle><stitle>Sci Rep</stitle><addtitle>Sci Rep</addtitle><date>2024-10-15</date><risdate>2024</risdate><volume>14</volume><issue>1</issue><spage>24208</spage><epage>13</epage><pages>24208-13</pages><artnum>24208</artnum><issn>2045-2322</issn><eissn>2045-2322</eissn><abstract>DNA methylation plays a significant role in regulating transcription and exhibits a systematic change with age. These changes can be used to predict an individual’s age. First, to identify methylation sites associated with biological age; second, to construct a biological age prediction model and preliminarily explore the biological significance of methylation-associated genes using machine learning. A biological age prediction model was constructed using human methylation data through data preprocessing, feature selection procedures, statistical analysis, and machine learning techniques. Subsequently, 15 methylation data sets were subjected to in-depth analysis using SHAP, GO enrichment, and KEGG analysis. XGBoost, LightGBM, and CatBoost identified 15 groups of methylation sites associated with biological age. The cg23995914 locus was identified as the most significant contributor to predicting biological age by calculating SHAP values. Furthermore, GO enrichment and KEGG analyses were employed to initially explore the methylated loci’s biological significance.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>39406876</pmid><doi>10.1038/s41598-024-75586-9</doi><tpages>13</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2045-2322 |
| ispartof | Scientific reports, 2024-10, Vol.14 (1), p.24208-13, Article 24208 |
| issn | 2045-2322 2045-2322 |
| language | eng |
| recordid | cdi_doaj_primary_oai_doaj_org_article_9357a049144045f0891a3ccb854302f4 |
| source | Full-Text Journals in Chemistry (Open access); Publicly Available Content (ProQuest); PubMed Central; Springer Nature - nature.com Journals - Fully Open Access |
| subjects | 631/114/1305 631/208/176/1988 Age Aging Aging - genetics Biological age Biomarkers CpG Islands Datasets Deoxyribonucleic acid DNA DNA Methylation Epigenetics Experimental methods Gender Gene expression Gene loci Genomes GO enrichment analysis Health care Humanities and Social Sciences Humans Interpretable machine learning Learning algorithms Machine Learning Male Medicine multidisciplinary Physiology Prediction models Research methodology Science Science (multidisciplinary) Shapley Additive exPlanations Statistical analysis Statistical models XGBoost |
| title | Exploring the correlation between DNA methylation and biological age using an interpretable machine learning framework |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T23%3A26%3A13IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Exploring%20the%20correlation%20between%20DNA%20methylation%20and%20biological%20age%20using%20an%20interpretable%20machine%20learning%20framework&rft.jtitle=Scientific%20reports&rft.au=Zhou,%20Sheng&rft.date=2024-10-15&rft.volume=14&rft.issue=1&rft.spage=24208&rft.epage=13&rft.pages=24208-13&rft.artnum=24208&rft.issn=2045-2322&rft.eissn=2045-2322&rft_id=info:doi/10.1038/s41598-024-75586-9&rft_dat=%3Cproquest_doaj_%3E3116761136%3C/proquest_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c422t-85e911dea84c5a11da588745d745293d5ba8a62701115cb64ab8e759a7fac6a23%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=3116761136&rft_id=info:pmid/39406876&rfr_iscdi=true |