A comparison of feature selection methodologies and learning algorithms in the development of a DNA methylation-based telomere length estimator

[1]  M. Levine,et al.  A computational solution for bolstering reliability of epigenetic clocks: implications for clinical trials and longitudinal tracking , 2022, Nature Aging.

[2]  Tao Huang,et al.  Identification of DNA Methylation Signature and Rules for SARS-CoV-2 Associated with Age. , 2022, Frontiers in bioscience.

[3]  S. Horvath,et al.  Telomere length and epigenetic clocks as markers of cellular aging: a comparative study , 2022, GeroScience.

[4]  Ritambhara Singh,et al.  A pan-tissue DNA-methylation epigenetic clock based on deep learning , 2021, npj Aging.

[5]  H. Lähdesmäki,et al.  Probabilistic modeling methods for cell-free DNA methylation based cancer classification , 2022, BMC Bioinformatics.

[6]  Correlation between telomere length and biomarkers of oxidative stress in human aging. , 2022, Rejuvenation research.

[7]  D. Belsky,et al.  DunedinPACE, a DNA methylation biomarker of the pace of aging , 2021, eLife.

[8]  A. Zhavoronkov,et al.  DeepMAge: A Methylation Aging Clock Developed with Deep Learning , 2021, Aging and disease.

[9]  Padraig Cunningham,et al.  Feature Selection Tutorial with Python Examples , 2021, ArXiv.

[10]  S. Horvath,et al.  DNA-methylation-based telomere length estimator: comparisons with measurements from flow FISH and qPCR , 2021, Aging.

[11]  M. Levine,et al.  A computational solution for bolstering reliability of epigenetic clocks: implications for clinical trials and longitudinal tracking , 2021, Nature Aging.

[12]  S. Horvath,et al.  DNA methylation aging and transcriptomic studies in horses , 2021, bioRxiv.

[13]  Pulung Hendro Prastyo,et al.  A Review of Feature Selection Techniques in Sentiment Analysis Using Filter, Wrapper, or Hybrid Methods , 2020, 2020 6th International Conference on Science and Technology (ICST).

[14]  K. Godfrey,et al.  Machine Learning-Based DNA Methylation Score for Fetal Exposure to Maternal Smoking: Development and Validation in Samples Collected from Adolescents and Adults , 2020, Environmental health perspectives.

[15]  E. Ballestar,et al.  Clinical value of DNA methylation markers in autoimmune rheumatic diseases , 2020, Nature Reviews Rheumatology.

[16]  Longlong Wang,et al.  Identification and validation of novel DNA methylation markers for early diagnosis of lung adenocarcinoma , 2020, Molecular oncology.

[17]  L. Brace,et al.  Highly accurate skin-specific methylome analysis algorithm as a platform to screen and validate therapeutics for healthy aging , 2020, Clinical Epigenetics.

[18]  V. Garg,et al.  Human age prediction using DNA methylation and regression methods , 2020, International Journal of Information Technology.

[19]  S. Ning,et al.  CancerClock: A DNA Methylation Age Predictor to Identify and Characterize Aging Clock in Pan-Cancer , 2019, Front. Bioeng. Biotechnol..

[20]  Wei Zhou,et al.  Integrative analysis of DNA methylation and gene expression identified cervical cancer-specific diagnostic biomarkers , 2019, Signal Transduction and Targeted Therapy.

[21]  B. Koes,et al.  Heritability of telomere length across three generations of Korean families , 2019, Pediatric Research.

[22]  S. Verhulst Improving comparability between qPCR‐based telomere studies , 2019, Molecular ecology resources.

[23]  Hojung Nam,et al.  Development of Tissue-Specific Age Predictors Using DNA Methylation Data , 2019, Genes.

[24]  S. Horvath,et al.  DNA methylation-based estimator of telomere length , 2019, Aging.

[25]  Cees G. M. Snoek,et al.  Variable Selection , 2019, Model-Based Clustering and Classification for Data Science.

[26]  David M. Blei,et al.  Dimension Reduction , 2019, Computer Vision, A Reference Guide.

[27]  R. Marioni,et al.  An epigenetic score for BMI based on DNA methylation correlates with poor physical health and major disease in the Lothian Birth Cohort , 2019, International Journal of Obesity.

[28]  D. Belsky,et al.  Establishing a generalized polyepigenetic biomarker for tobacco smoking , 2019, Translational Psychiatry.

[29]  Joe Naoum-Sawaya,et al.  Optimization Models for Machine Learning: A Survey , 2019, Eur. J. Oper. Res..

[30]  Eliseos J. Mucaki,et al.  Predicting Response to Platin Chemotherapy Agents with Biochemically-inspired Machine Learning , 2017, bioRxiv.

[31]  Tellervo Korhonen,et al.  EpiSmokEr: A robust classifier to determine smoking status from DNA methylation data , 2018, bioRxiv.

[32]  Keegan D. Korthauer,et al.  A practical guide to methods controlling false discoveries in computational biology , 2018, Genome Biology.

[33]  C. Lewis,et al.  DNA methylation and inflammation marker profiles associated with a history of depression , 2018, Human molecular genetics.

[34]  Alex P. Reiner,et al.  Epigenetic clock for skin and blood cells applied to Hutchinson Gilford Progeria Syndrome and ex vivo studies , 2018, Aging.

[35]  D. Belsky LIFE-COURSE LONGITUDINAL STUDIES ARE NEEDED TO ADVANCE INTEGRATION OF GENOMICS AND SOCIAL EPIDEMIOLOGY. , 2018, American journal of epidemiology.

[36]  S. Horvath,et al.  DNA methylation-based biomarkers and the epigenetic clock theory of ageing , 2018, Nature Reviews Genetics.

[37]  R. Marioni,et al.  The epigenetic clock and telomere length are independently associated with chronological age and mortality , 2017, International journal of epidemiology.

[38]  Richie Poulton,et al.  Eleven Telomere, Epigenetic Clock, and Biomarker-Composite Quantifications of Biological Aging: Do They Measure the Same Thing? , 2017, American journal of epidemiology.

[39]  M. Yeager,et al.  Effect of pre-analytic variables on the reproducibility of qPCR relative telomere length measurement , 2017, PloS one.

[40]  S. Horvath,et al.  Leukocyte telomere length, T cell composition and DNA methylation age , 2017, Aging.

[41]  J. Bakdash,et al.  Repeated Measures Correlation , 2017, Front. Psychol..

[42]  R. Marioni,et al.  Epigenetic Signatures of Cigarette Smoking , 2016, Circulation. Cardiovascular genetics.

[43]  Robin M. Murray,et al.  An integrated genetic-epigenetic analysis of schizophrenia: evidence for co-localization of genetic associations and differential DNA methylation , 2016, Genome Biology.

[44]  M. Levine,et al.  An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease , 2016, Genome Biology.

[45]  R. Marioni,et al.  The epigenetic clock and telomere length are independently associated with chronological age and mortality , 2016, International journal of epidemiology.

[46]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[47]  Bernd Holleczek,et al.  Frailty is associated with the epigenetic clock but not with telomere length in a German cohort , 2016, Clinical Epigenetics.

[48]  Serdar Bozdag,et al.  A Feature Selection Algorithm to Compute Gene Centric Methylation from Probe Level Methylation Data , 2016, PloS one.

[49]  Muhammad Hisyam Lee,et al.  Penalized logistic regression with the adaptive LASSO for gene selection in high-dimensional cancer classification , 2015, Expert Syst. Appl..

[50]  Lan Hu,et al.  A novel strategy for forensic age prediction by DNA methylation and support vector regression model , 2015, Scientific Reports.

[51]  R. Mayeux,et al.  Heritability of telomere length in a study of long-lived families , 2015, Neurobiology of Aging.

[52]  C. Dalgård,et al.  Leukocyte telomere length dynamics in women and men: menopause vs age effects , 2015, International journal of epidemiology.

[53]  S. K. Merid,et al.  DNA methylation loci associated with atopy and high serum IgE: a genome-wide application of recursive Random Forest feature selection , 2015, Genome Medicine.

[54]  Gabrielle A. Lockett,et al.  DNA methylation loci associated with atopy and high serum IgE: a genome-wide application of recursive Random Forest feature selection , 2015, Genome Medicine.

[55]  R. Decorte,et al.  Improved age determination of blood and teeth samples using a selected set of DNA methylation markers , 2015, Epigenetics.

[56]  J. Holloway,et al.  Identifying CpG sites associated with eczema via random forest screening of epigenome-scale DNA methylation , 2015, Clinical Epigenetics.

[57]  Nikola Bogunovic,et al.  A review of feature selection methods with applications , 2015, 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).

[58]  Richie Poulton,et al.  The Dunedin Multidisciplinary Health and Development Study: overview of the first 40 years, with an eye to the future , 2015, Social Psychiatry and Psychiatric Epidemiology.

[59]  S. Humphries,et al.  Telomere shortening over 6 years is associated with increased subclinical carotid vascular damage and worse cardiovascular prognosis in the general population , 2015, Journal of internal medicine.

[60]  C. Dalgård,et al.  The heritability of leucocyte telomere length dynamics , 2015, Journal of Medical Genetics.

[61]  Dong Xu,et al.  Classification of lung cancer using ensemble-based feature selection and machine learning methods. , 2015, Molecular bioSystems.

[62]  Ljubomir J. Buturovic,et al.  Cross-validation pitfalls when selecting and assessing regression and classification models , 2014, Journal of Cheminformatics.

[63]  Thomas W. Mühleisen,et al.  Aging of blood can be tracked by DNA methylation changes at just three CpG sites , 2014, Genome Biology.

[64]  Jack A. Taylor,et al.  Genome-wide age-related DNA methylation changes in blood and other tissues relate to histone modification, expression and cancer. , 2014, Carcinogenesis.

[65]  S. Horvath DNA methylation age of human tissues and cell types , 2013, Genome Biology.

[66]  A. Caspi,et al.  Exposure to violence during childhood is associated with telomere erosion from 5 to 10 years of age: a longitudinal study , 2013, Molecular Psychiatry.

[67]  Ruth Pidsley,et al.  A data-driven approach to preprocessing Illumina 450K methylation array data , 2013, BMC Genomics.

[68]  Kang Tai,et al.  Comparison of statistical and machine learning methods in modelling of data with multicollinearity , 2013, Int. J. Model. Identif. Control..

[69]  Hermann Brenner,et al.  A systematic review of leukocyte telomere length and age in adults , 2013, Ageing Research Reviews.

[70]  T. Ideker,et al.  Genome-wide methylation profiles reveal quantitative views of human aging rates. , 2013, Molecular cell.

[71]  T. Spector,et al.  Meta-analysis of telomere length in 19 713 subjects reveals high heritability, stronger maternal inheritance and a paternal age effect , 2013, European Journal of Human Genetics.

[72]  J. Ogutu,et al.  Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions , 2012, BMC Proceedings.

[73]  Devin C. Koestler,et al.  DNA methylation arrays as surrogate measures of cell mixture distribution , 2012, BMC Bioinformatics.

[74]  Steven E. Franklin,et al.  A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery , 2012 .

[75]  K. Gunderson,et al.  High density DNA methylation array with single CpG site resolution. , 2011, Genomics.

[76]  W. Wagner,et al.  Epigenetic-aging-signature to determine age in different tissues , 2011, Aging.

[77]  Steve Horvath,et al.  Epigenetic Predictor of Age , 2011, PloS one.

[78]  D. Grobbee,et al.  The (mis)use of overlap of confidence intervals to assess effect modification , 2011, European Journal of Epidemiology.

[79]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[80]  M. Fenech,et al.  A quantitative PCR method for measuring absolute telomere length , 2011, Biological Procedures Online.

[81]  A. Aviv,et al.  Longitudinal versus Cross-sectional Evaluations of Leukocyte Telomere Length Dynamics: Age-Dependent Telomere Shortening is the Rule , 2011, The journals of gerontology. Series A, Biological sciences and medical sciences.

[82]  M. Goniewicz,et al.  Biomarkers increase detection of active smoking and secondhand smoke exposure in critically ill patients* , 2011, Critical care medicine.

[83]  H. Abdi,et al.  Principal component analysis , 2010 .

[84]  F. Kronenberg,et al.  Influences on the reduction of relative telomere length over 10 years in the population-based Bruneck Study: introduction of a well-controlled high-throughput assay. , 2009, International journal of epidemiology.

[85]  N. Benowitz,et al.  Prevalence of smoking assessed biochemically in an urban public hospital: a rationale for routine cotinine screening. , 2009, American journal of epidemiology.

[86]  Giles M. Foody,et al.  Sample size determination for image classification accuracy assessment and comparison , 2009 .

[87]  Richard Simon,et al.  Bias in error estimation when using cross-validation for model selection , 2006, BMC Bioinformatics.

[88]  J. Vaupel,et al.  The Heritability of Telomere Length Among the Elderly and Oldest-Old , 2005, Twin Research and Human Genetics.

[89]  Marko Grobelnik,et al.  Feature Selection Using Support Vector Machines , 2002 .

[90]  John D. Storey A direct approach to false discovery rates , 2002 .

[91]  Peter C Austin,et al.  A brief note on overlapping confidence intervals. , 2002, Journal of vascular surgery.

[92]  R. Cawthon Telomere measurement by quantitative PCR. , 2002, Nucleic acids research.

[93]  J. Skurnick,et al.  Telomere Length as an Indicator of Biological Aging: The Gender Effect and Relation With Pulse Pressure and Pulse Wave Velocity , 2001, Hypertension.

[94]  J. Skurnick,et al.  Telomere length inversely correlates with pulse pressure and is highly familial. , 2000, Hypertension.

[95]  Richard Bellman,et al.  Adaptive Control Processes: A Guided Tour , 1961, The Mathematical Gazette.

[96]  J. Hopper,et al.  DNA Methylation–Based Measures of Biological Aging , 2018 .

[97]  Amr Badr,et al.  Feature Selection and Extraction Framework for DNA Methylation in Cancer , 2017 .

[98]  Richard Bellman,et al.  Adaptive Control Processes - A Guided Tour (Reprint from 1961) , 2015, Princeton Legacy Library.

[99]  Hervé Abdi,et al.  Wiley Interdisciplinary Reviews: Computational Statistics , 2010 .

[100]  Rosemary A. Renaut,et al.  Computational Statistics and Data Analysis , 2022 .

[101]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[102]  L. Breiman Random Forests , 2001, Machine Learning.

[103]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[104]  Pedro Larrañaga,et al.  A review of feature selection techniques in bioinformatics , 2007, Bioinform..