পদ্ধতির তুলনা করুন

	এমএল-সহায়ক এপিজেনোম-ওয়াইড অ্যাসোসিয়েশন স্টাডি (ML-EWAS)	ল্যাসো রিগ্রেশন
ক্ষেত্র	জৈব তথ্যবিজ্ঞান	যন্ত্র শিখন
পরিবার	Process / pipeline	Machine learning
উদ্ভবের বছর	2010s (methodological consolidation ~2015–2020)	1996
প্রবর্তক	Teschendorff, Relton, and others in the epigenomics field	Tibshirani, R.
ধরন	Integrative omics analysis pipeline	Regularized linear regression (L1 penalty)
মৌলিক উৎস	Teschendorff, A. E., & Relton, C. L. (2018). Statistical and integrative system-level analysis of DNA methylation data. Nature Reviews Genetics, 19(3), 129–147.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
অপর নাম	ML-EWAS, machine learning EWAS, ML-assisted EWAS, epigenome-wide association study with machine learning	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
সম্পর্কিত	3	4
সারসংক্ষেপ	Machine learning-assisted EWAS integrates conventional epigenome-wide association testing with machine learning models to identify DNA methylation sites associated with a phenotype of interest. By combining the statistical rigour of EWAS with the pattern-recognition power of algorithms such as elastic net, random forest, or gradient boosting, this approach handles the extreme dimensionality of methylation arrays (450,000–850,000 CpG sites) more effectively than univariate testing alone, and can capture non-linear and interaction effects that standard linear models miss.	Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter.