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A deep learning model for early risk prediction of heart failure with preserved ejection fraction by DNA methylation profiles combined with clinical features

Xuetong Zhao, Yang Sui, Xiuyan Ruan, Xinyue Wang, Kunlun He, Wei Dong, Hongzhu Qu, Xiangdong Fang
2022 Clinical Epigenetics  
clinical characteristics or DNA methylation levels alone, published chronic heart failure risk prediction models and other benchmark machine learning models.  ...  Here, we have developed a deep learning framework, HFmeRisk, using both 5 clinical features and 25 DNA methylation loci to predict the early risk of HFpEF in the Framingham Heart Study Cohort.  ...  We thank the staff and professors of the Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital, and Harvard Medical School.  ... 
doi:10.1186/s13148-022-01232-8 pmid:35045866 pmcid:PMC8772140 fatcat:tyk6agxqrjh5xlqzcsc4kqkfii

DNA methylation and gene expression integration in cardiovascular disease

Guillermo Palou-Márquez, Isaac Subirana, Lara Nonell, Alba Fernández-Sanlés, Roberto Elosua
2021 Clinical Epigenetics  
Moreover, two of these factors improved the predictive capacity of a classical risk function.  ...  Results Four independent latent factors (9, 19, 21—only in women—and 27), driven by DNA methylation, were associated with cardiovascular disease independently of classical risk factors and cell-type counts  ...  Acknowledgements We thank Ricard Argelaguet for his comments and help with the MOFA analyses and Elaine M. Lilly, Ph.D., for her critical reading and revision of the English text.  ... 
doi:10.1186/s13148-021-01064-y pmid:33836805 pmcid:PMC8034168 fatcat:3hi4ahn4qrfw5kmg5jzzipkwk4

Artificial Intelligence and Cardiovascular Genetics

Chayakrit Krittanawong, Kipp W. Johnson, Edward Choi, Scott Kaplin, Eric Venner, Mullai Murugan, Zhen Wang, Benjamin S. Glicksberg, Christopher I. Amos, Michael C. Schatz, Wilson Tang
2022 Life  
Advances will likely be achieved through increasingly frequent and robust genomic characterization of patients, as well the integration of genomic data with other clinical data, such as cardiac imaging  ...  A major goal of cardiovascular medicine has been to understand how genetic variation leads to the clinical heterogeneity seen in polygenic cardiovascular diseases (CVDs).  ...  Acknowledgments: The authors would like to thank Ishan Kamat and Jennifer Wilcox for their suggestions and comments on this article.  ... 
doi:10.3390/life12020279 pmid:35207566 pmcid:PMC8875522 fatcat:qlg3hjsmejdjrjl32vh3wp45ya

Leveraging Existing Cohorts to Study Health Effects of Air Pollution on Cardiometabolic Disorders: India Global Environmental and Occupational Health Hub

Gagandeep K Walia, Siddhartha Mandal, Suganthi Jaganathan, Lindsay M Jaacks, Nancy L Sieber, Preet K Dhillon, Bhargav Krishna, Melina S Magsumbol, Kishore K Madhipatla, Dimple Kondal, Richard A Cash, K Srinath Reddy (+2 others)
2020 Environmental Health Insights  
The other exploratory aims are to explore mediatory role of the epigenetic mechanisms (DNA methylation) and vitamin D exposure in determining the association between air pollution exposure and cardiovascular  ...  risk factors and disease outcomes.  ...  We would also like to acknowledge all the members of the GEOHealth Team for their contributions. Ethical Approval The The Global Environmental and Occupational Health (GEOHealth) Team  ... 
doi:10.1177/1178630220915688 pmid:32341651 pmcid:PMC7171984 fatcat:jaus4slu25dhxkbaxwzbzkgmda

Artificial intelligence and leukocyte epigenomics: Evaluation and prediction of late-onset Alzheimer's disease

Ray O Bahado-Singh, Sangeetha Vishweswaraiah, Buket Aydas, Ali Yilmaz, Raghu P Metpally, David J Carey, Richard C Crist, Wade H Berrettini, George D Wilson, Khalid Imam, Michael Maddens, Halil Bisgin (+2 others)
2021 PLoS ONE  
The differentially methylated genes CTSV & PRMT5 (ventricular hypertrophy and dilation) are linked to cardiovascular disease and of interest given the known association between impaired cerebral blood  ...  Genome-wide DNA methylation analysis was performed using the Infinium MethylationEPIC BeadChip array in 24 late-onset AD (LOAD) and 24 cognitively healthy subjects.  ...  Machine Learning (ML) is a branch of AI that focuses on computer learning and adapting from a set of data with which it has been presented.  ... 
doi:10.1371/journal.pone.0248375 pmid:33788842 pmcid:PMC8011726 fatcat:nfukbbwa2nf4heu3eqtehf65c4

Brain age predicts mortality

J H Cole, S J Ritchie, M E Bastin, M C Valdés Hernández, S Muñoz Maniega, N Royle, J Corley, A Pattie, S E Harris, Q Zhang, N R Wray, P Redmond (+6 others)
2017 Molecular Psychiatry  
Furthermore, while combining brain-predicted age with grey matter and cerebrospinal fluid volumes (themselves strong predictors) not did improve mortality risk prediction, the combination of brain-predicted  ...  Brain-predicted age was calculated using machine-learning analysis, trained on neuroimaging data from a large healthy reference sample (N = 2001), then tested in the Lothian Birth Cohort 1936 (N = 669)  ...  The combination of DNA-methylation-predicted age and neuroimaging-predicted age is also novel.  ... 
doi:10.1038/mp.2017.62 pmid:28439103 pmcid:PMC5984097 fatcat:dfrtajqvnzfydnam3aw64vn6xm

Patient Similarity Networks for Precision Medicine

Shraddha Pai, Gary D. Bader
2018 Journal of Molecular Biology  
Achieving the goal of routine precision medicine that takes advantage of this rich genomics data will require computational methods that support heterogeneous data, have excellent predictive performance  ...  Traditional machine-learning approaches excel at performance, but often have limited interpretability.  ...  National Institutes of Health, National Center for Research Resources grant number P41 GM103504). We thank Ruth Isserlin for providing prepared data for the ependymoma classification example.  ... 
doi:10.1016/j.jmb.2018.05.037 pmid:29860027 pmcid:PMC6097926 fatcat:v6eucwccdjabthfefnzfddtvai

Artificial Intelligence and Medicine: A literature review [article]

Chottiwatt Jittprasong
2022 arXiv   pre-print
In practically every industry today, artificial intelligence is one of the most effective ways for machines to assist humans.  ...  This review of the literature will highlight the emerging field of artificial intelligence in medicine and its current level of development.  ...  Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases (Jurmeister et al. 2019) The study developed a machine learning  ... 
arXiv:2205.00322v2 fatcat:5f2qcmezjrajbok56xdnl5n4ou

DunedinPACE, a DNA methylation biomarker of the pace of aging

Daniel W Belsky, Avshalom Caspi, David L Corcoran, Karen Sugden, Richie Poulton, Louise Arseneault, Andrea Baccarelli, Kartik Chamarti, Xu Gao, Eilis Hannon, Hona Lee Harrington, Renate Houts (+8 others)
2022 eLife  
Here we report a next-generation DNA-methylation biomarker of Pace of Aging, DunedinPACE (for Pace of Aging Calculated from the Epigenome).Methods: We used data from the Dunedin Study 1972-3 birth cohort  ...  We distilled this two-decade Pace of Aging into a single-time-point DNA-methylation blood-test using elastic-net regression and a DNA-methylation dataset restricted to exclude probes with low test-retest  ...  The refined DNA-methylation dataset used for machine-learning improves reliability of measurement.  ... 
doi:10.7554/elife.73420 pmid:35029144 pmcid:PMC8853656 fatcat:chxxqxndkfgzpfg3ehu5lusplu

Machine learning selected smoking-associated DNA methylation signatures that predict HIV prognosis and mortality

Xinyu Zhang, Ying Hu, Bradley E. Aouizerat, Gang Peng, Vincent C. Marconi, Michael J. Corley, Todd Hulgan, Kendall J. Bryant, Hongyu Zhao, John H. Krystal, Amy C. Justice, Ke Xu
2018 Clinical Epigenetics  
The application of a machine learning approach to the analysis of CpG methylation in the epigenome enables the selection of phenotypically relevant features from high-dimensional data.  ...  Using this approach, we now report that a set of smoking-associated DNA-methylated CpGs predicts HIV prognosis and mortality in an HIV-positive veteran population.  ...  Acknowledgements The authors appreciate the support of the Veteran Aging Study Cohort Biomarker Core and Yale Center of Genomic Analysis.  ... 
doi:10.1186/s13148-018-0591-z pmid:30545403 pmcid:PMC6293604 fatcat:tpzbayqvwncppml2bkcfasajpq

Using Machine Learning to Predict Obesity Based on Genome-Wide and Epigenome-Wide Gene–Gene and Gene–Diet Interactions

Yu-Chi Lee, Jacob J. Christensen, Laurence D. Parnell, Caren E. Smith, Jonathan Shao, Nicola M. McKeown, José M. Ordovás, Chao-Qiang Lai
2022 Frontiers in Genetics  
In conclusion, we developed an integrated approach with ML to predict obesity using omics and dietary data.  ...  After identifying genetic, epigenetic, and dietary factors that passed statistical significance, we applied machine learning (ML) algorithms to predict participants' obesity status in the test set, taken  ...  I., and Habib, M. T. (2021). A Machine Learning Approach for Obesity Risk Prediction. Curr. Res. Behav.  ... 
doi:10.3389/fgene.2021.783845 pmid:35047011 pmcid:PMC8763388 fatcat:qa47rl4bkjafffnbpntsr6d66i

Genetic and epigenetic variations in BDNF gene involved in Anorexia Nervosa

N. Ramoz, G. Maussion, J. Clarke, P. Gorwood
2022 European psychiatry  
Objectives This work study the impact of the functional polymorphism at risk rs6265, epigenetic variations in DNA methylation of BDNF gene and consequences on the concentrations of BDNF in AN patients.  ...  Methods DNA was isolated from 24 AN patients and 48 controls. DNA methylation was measured for sites spanning the BDNF gene using Infinium HumanMethylation450 BeadChip technology.  ...  Objectives: To predict which patients develop cardiovascular disease using machine learning.  ... 
doi:10.1192/j.eurpsy.2022.1743 fatcat:ibd3mdvqrjgddanibx6ygr2qlm

Further Introduction of DNA Methylation (DNAm) Arrays in Regular Diagnostics

M. M. A. M. Mannens, M. P. Lombardi, M. Alders, P. Henneman, J. Bliek
2022 Frontiers in Genetics  
With the introduction of DNA methylation (DNAm) arrays such as the Illumina Infinium HumanMethylation450 Beadchip array or the Illumina Infinium Methylation EPIC Beadchip array (850 k), it has become feasible  ...  The successful use of such DNAm tests is rapidly expanding. More and more disorders are found to be associated with discrete episignatures which enables fast and definite diagnoses, as we have shown.  ...  DNA methylation profiling has significantly improved risk stratification in patients with adult brain tumors (Jaunmuktane et al., 2019) .  ... 
doi:10.3389/fgene.2022.831452 pmid:35860466 pmcid:PMC9289263 fatcat:izpln3hpijaeppqvtnpxq4mg2e

Quantification of the pace of biological aging in humans through a blood test: the DunedinPACE DNA methylation algorithm [article]

Daniel W Belsky, Avshalom Caspi, David L Corcoran, Karen Sugden, Richie Poulton, Louise Arseneault, Andrea Baccarelli, K Chamarti, Xu Gao, Eilis Hannon, HonaLee Harrington, Renate Houts (+8 others)
2021 medRxiv   pre-print
We distilled two-decade Pace of Aging into a single-time-point DNA-methylation blood-test using elastic-net regression and DNA-methylation data restricted to exclude probes with low test-retest reliability  ...  The resulting measure, DunedinPACE, showed high test-retest reliability, was associated with functional decline, morbidity, and mortality, and indicated accelerated Pace of Aging in young adults with childhood  ...  Acknowledgement This research was supported by US-National Institute on Aging grants AG032282, AG061378, AG066887, and UK Medical Research Council grant MR/P005918/1.  ... 
doi:10.1101/2021.08.30.21262858 fatcat:qqu5kwkzd5abpkiib3jrwy3cf4

Machine Learning in Multi-Omics Data to Assess Longitudinal Predictors of Glycaemic Trait Levels [article]

Laurie Prelot, Harmen Draisma, Mila Desi Anasanti, Zhanna Balkhiyarova, Matthias Wielscher, Loic Yengo, Sylvain Sebert, Mika Ala-Korpela, Philippe Froguel, Marjo-Riitta Jarvelin, Marika Kaakinen, Inga Prokopenko
2018 bioRxiv   pre-print
Addition of methylation data, did not improve the predictions (P>0.3, model comparison); however, 15/17 markers were amongst the top 25 predictors of FI/FG when using Mb-S+Mh-R data.  ...  We aimed to identify longitudinal predictors of glycaemic traits relevant for T2D by applying machine learning (ML) to multi-omics data from the Northern Finland Birth Cohort 1966 at 31 (T1) and 46 (T2  ...  The potential of novel biomarkers to 567 improve risk prediction of type 2 diabetes. Diabetologia 57, 16-29 (2014). , K. et al.  ... 
doi:10.1101/358390 fatcat:kpi2fpsvdrcebhxnbv57tlxyqq
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