Machine Learning Application in Genomic, Exercise, and Vital Datasets
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
PURPOSE: Machine learning (ML) refers to newly developed computer algorithms that are improved through iterative experiences. ML applications are expected to assist humans in analyzing large amounts of data. This review has outlined the application of ML in analyzing variable vital data such as walking steps, exercise intensity, heart rate, sleeping hours, sleep quality, resting heart rate, blood pressure, and calorie consumption in a day. Vital data consist of different variables that are closely related to genomic or exercise data. The prediction of healthy traits from a vital dataset has become a necessity in personalized medicine. METHODS: Considerations and repeated tasks in supervised, semi-supervised, and unsupervised ML methods are presented. ML methods such as artificial neural networks, Bayesian networks, support vector machines, and decision trees have been widely used in biomedical studies to develop predictive models. Through vital data, these models can help in effective and accurate decision-making for a healthier life. PURPOSE: Models based on genomic, exercise, and vital datasets provide a healthy lifestyle through regular exercise. We have provided guidelines to help in the selection of these ML methods and their practical application for variable vital data analysis. CONCLUSIONS: Our guidelines could serve as a foundation for implementing both participatory medicine and data-driven exercise science.
Abstract
PURPOSE: Machine learning (ML) refers to newly developed computer algorithms that are improved through iterative experiences. ML applications are expected to assist humans in analyzing large amounts of data. This review has outlined the application of ML in analyzing variable vital data such as walking steps, exercise intensity, heart rate, sleeping hours, sleep quality, resting heart rate, blood pressure, and calorie consumption in a day. Vital data consist of different variables that are closely related to genomic or exercise data. The prediction of healthy traits from a vital dataset has become a necessity in personalized medicine. METHODS: Considerations and repeated tasks in supervised, semi-supervised, and unsupervised ML methods are presented. ML methods such as artificial neural networks, Bayesian networks, support vector machines, and decision trees have been widely used in biomedical studies to develop predictive models. Through vital data, these models can help in effective and accurate decision-making for a healthier life. PURPOSE: Models based on genomic, exercise, and vital datasets provide a healthy lifestyle through regular exercise. We have provided guidelines to help in the selection of these ML methods and their practical application for variable vital data analysis. CONCLUSIONS: Our guidelines could serve as a foundation for implementing both participatory medicine and data-driven exercise science.
참고문헌 (57)
- Hinkson IV, "A Comprehensive Infrastructure for big data in cancer research : accelerating cancer research and precision medicine", Front Cell Dev Biol, 5, pp.83~ (2017)
- Obermeyer Z, "Predicting the future-big data, machine learning, and clinical medicine", N Engl J Med, 375(13), pp.1216~1219 (2016)
- He KY, "Big data analytics for genomic medicine", Int J Mol Sci, 18(2), pp.412~ (2017)
- 이충호, "Medical big data: promise and challenges", Kidney Research and Clinical Practice, 36(1), pp.3~11 (2017), 대한신장학회
- Mostafa Haghi, "Wearable Devices in Medical Internet of Things: Scientific Research and Commercially Available Devices", Healthcare Informatics Research, 23(1), pp.4~15 (2017), 대한의료정보학회[DOI]
- Yang H, "User acceptance of wearable devices : an extended perspective of perceived value", Telemat Inform, 33(2), pp.256~269 (2016)
- Goodwin S, "Coming of age : ten years of next-generation sequencing technologies", Nat Rev Genet, 17(6), pp.333~351 (2016)
- Sirbu A, "RNA-Seq vs dual-and singlechannel microarray data : sensitivity analysis for differential expression and clustering", PLoS One, 7(12), pp.e50986~ (2012)
- Anaissi A, "Ensemble feature learning of genomic data using support vector machine", PLoS One, 11(6), pp.e0157330~ (2016)
- Bibault JE, "Big data and machine learning in radiation oncology : state of the art and future prospects", Cancer Lett, 382(1), pp.110~117 (2016)
- Barrett T, "NCBI GEO: archive for functional genomics data sets--update", Nucleic Acids Res, 41(Database issue), pp.D991~D995 (2013)
- Kolesnikov N, "Arrayexpress update--simplifying data submissions", Nucleic Acids Res, 43(Database issue), pp.D1113~D1116 (2015)
- Hecker M, "Aberrant expression of alternative splicing variants in multiple sclerosis-a systematic review", Autoimmun Rev, 18(7), pp.721~732 (2019)
- Nakhuda A, "Biomarkers of browning of white adipose tissue and their regulation during exercise-and diet-induced weight loss", Am J Clin Nutr, 104(3), pp.557~565 (2016)
- Diao JA, "Biomedical informatics and machine learning for clinical genomics", Hum Mol Genet, 27(R1), pp.R29~R34 (2018)
- Libbrecht MW, "Machine learning applications in genetics and genomics", Nat Rev Genet, 16(6), pp.321~332 (2015)
- de Bruijne M, "Machine learning approaches in medical image analysis : From detection to diagnosis", Med Image Anal, 33, pp.94~97 (2016)
- Grys BT, "Machine learning and computer vision approaches for phenotypic profiling", J Cell Biol, 216(1), pp.65~71 (2017)
- Mirzaei G, "Imaging and machine learning techniques for diagnosis of Alzheimer's disease", Rev Neurosci, 27(8), pp.857~870 (2016)
- Cho S-B, "Machine learning in DNA microarray analysis for cancer classification", Proceedings of the First Asia-Pacific Bioinformatics Conference on Bioinformatics, 19 (2003)
- Tan AC, "Ensemble machine learning on gene expression data for cancer classification", Appl Bioinformatics, 2(3 Suppl), pp.S75~S83 (2003)
- Wang Y, "Gene selection from microarray data for cancer classification--a machine learning approach", Comput Biol Chem, 29(1), pp.37~46 (2005)
- Pirooznia M, "A comparative study of different machine learning methods on microarray gene expression data", BMC Genomics, 9(Suppl 1), pp.S13~ (2008)
- Qin J, "Kernel hierarchical gene clustering from microarray expression data", Bioinformatics, 19(16), pp.2097~2104 (2003)
- Horng JT, "An expert system to classify microarray gene expression data using gene selection by decision tree", Expert Syst Appl, 36(5), pp.9072~9081 (2009)
- Williams-DeVane CR, "Decision tree-based method for integrating gene expression, demographic, and clinical data to determine disease endotypes", BMC Syst Biol, 7(1), pp.119~ (2013)
- Shaikhina T, "Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation", Biomed Signal Process Control, 52, pp.456~462 (2019)
- Bernard S, "On the selection of decision trees in random forests", 2009 International Joint Conference on Neural Networks (2009), The Institute of Electrical and Electronics Engineers (IEEE)
- Shinde SR, "A genetic algorithm, information gain and artificial neural network based approach for hypertension diagnosis", 2016 International Conference on Inventive Computation Technologies (ICICT) (2016)
- Liu X, "Transfer learning with convolutional neural network for early gastric cancer classification on magnifiying narrow-band imaging images", 2018 25th IEEE International Conference on Image Processing (ICIP) (2018)
- Oh SL, "A deep learning approach for parkinson’s disease diagnosis from EEG signals", Neural Comput Appl, 32(15), pp.10927~10933 (2020)
- Zhou J, "Predicting effects of noncoding variants with deep learning-based sequence model", Nat Methods, 12(10), pp.931~934 (2015)
- Poplin R, "A universal SNP and small-indel variant caller using deep neural networks", Nat Biotechnol, 36(10), pp.983~987 (2018)
- Jaganathan K, "Predicting splicing from primary sequence with deep learning", Cell, 176(3), pp.535~48 e524 (2019)
- Lee Y, "Classification of multiple cancer types by multicategory support vector machines using gene expression data", Bioinformatics, 19(9), pp.1132~1139 (2003)
- Guyon I, "Gene selection for cancer classification using support vector machines", Mach Learn, 46(1), pp.389~422 (2002)
- Golub TR, "Molecular classification of cancer : class discovery and class prediction by gene expression monitoring", Science, 286(5439), pp.531~537 (1999)
- Moler EJ, "Analysis of molecular profile data using generative and discriminative methods", Physiol Genomics, 4(2), pp.109~126 (2000)
- Dai JJ, "Dimension reduction for classification with gene expression microarray data", Stat Appl Genet Mol Biol, 5 (2006)
- Misra J, "Interactive exploration of microarray gene expression patterns in a reduced dimensional space", Genome Res, 12(7), pp.1112~1120 (2002)
- Musani SK, "Principal component analysis of quantitative trait loci for immune response to adenovirus in mice", Hereditas, 143(2006), pp.189~197 (2006)
- McVean G, "A genealogical interpretation of principal components analysis", PLoS Genet, 5(10), pp.e1000686~ (2009)
- Duforet-Frebourg N, "Detecting genomic signatures of natural selection with principal component analysis : application to the 1000 Genomes Data", Mol Biol Evol, 33(4), pp.1082~1093 (2016)
- Weintraub WS, "Translational medicine in the era of big data and machine learning", Circ Res, 123(11), pp.1202~1204 (2018)
- Low SK, "Breast cancer : The translation of big genomic data to cancer precision medicine", Cancer Sci, 109(3), pp.497~506 (2018)
- Li Y, "A review on machine learning principles for multi-view biological data integration", Brief Bioinform, 19(2), pp.325~340 (2018)
- Mirza B, "Machine learning and integrative analysis of biomedical big data", Genes (Basel), 10(2) (2019)
- Hicks SC, "Missing data and technical variability in single-cell RNA-sequencing experiments", Biostatistics, 19(4), pp.562~578 (2018)
- Cai J, "Feature selection in machine learning : A new perspective", Neurocomputing, 300, pp.70~79 (2018)
- Ali M, "Machine learning and feature selection for drug response prediction in precision oncology applications", Biophys Rev, 11(1), pp.31~39 (2019)
- Suh C, "Evolving the materials genome : how machine learning is gueling the next generation of materials discovery", Annual Review of Materials Research, 50, pp.1~25 (2020)
- Melov S, "Resistance exercise reverses aging in human skeletal muscle", PLoS One, 2(5), pp.e465~ (2007)
- Raue U, "Transcriptome signature of resistance exercise adaptations: mixed muscle and fiber type specific profiles in young and old adults", J Appl Physiol. (1985), 112(10), pp.1625~1636 (2012)
- Stewart J, "Artificial intelligence and machine learning in emergency medicine", Emerg Med Australas, 30(6), pp.870~874 (2018)
- Vissing K, "Simplified data access on human skeletal muscle transcriptome responses to differentiated exercise", Sci Data, 1, pp.140041~ (2014)
- Pillon NJ, "Transcriptomic profiling of skeletal muscle adaptations to exercise and inactivity", Nat Commun, 11(1), pp.470~ (2020)
- 백경완, "Relationship Between METTL21C Gene Expression and Exercise in Human Skeletal Muscle: A Meta-Analysis", 운동과학, 30(1), pp.102~109 (2021), 한국운동생리학회
