A comparison of feature selection methodologies and learning algorithms in the development of a DNA methylation-based telomere length estimator
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Sarah Jane Delany | A. Caspi | D. Corcoran | K. Sugden | R. Poulton | T. Moffitt | J. Mill | E. Dempster | E. Hannon | T. Murphy | Ben Williams | Trevor Doherty | Therese M. Murphy
[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..