TABLE 1.

List of available AI and ML-based tools used for epigenetic studies in RDs.

Function	References	Software/Platform/Algorithm	AI/ML method	Disease(s)	Classification
Annotates and prioritizes non-coding regulatory variants	Fu et al., 2014	FunSeq2 http://funseq2.gersteinlab.org/	Scoring scheme, using conservation, regulatory, and other measures	Medulloblastoma	Supervised/Unsupervised
Discover variants associated to specific Mendelian disorders	Smedley et al., 2016	Genomiser https://hpo.jax.org/app/tools/genomiser	ReMM framework/RF classifier	Beckwith-Wiedemann syndrome (ORPHA:116), beta thalassemia (ORPHA:848), Marie Unna hereditary hypotrichosis (ORPHA:444)	Supervised
Causal variant analysis and identification	Farh et al., 2015	PICS	Bayesian approaches	Immune disorders	Supervised
Predict the effect of regulatory variation	Vuckovic et al., 2020	Delta SVM http://www.beerlab.org/deltasvm/	SVM classifier	Blood cell traits	Supervised
Genes and gene sets prediction	Hou et al., 2017	GeneMANIA https://genemania.org/	Fast heuristic algorithm derived from ridge regression	RVF	Supervised/Unsupervised
miRNA target prediction and functional annotation		miRDB	MirTarget
Detect statistically significant interaction events in Capture HiC data	McMaster et al., 2018	CHiCAGO (http://regulatorygenomicsgroup.org/chicago)	Convolution background model	Waldenstrom macroglobulinemia	Supervised
Identifies the precise location of active TREs	Chu et al., 2018	dREG.HDhttps://github.com/Danko-Lab/dREG.HD	Epsilon SVR with a Gaussian kernel	Human glioblastoma	Supervised
Genotype/phenotype data analysis	Luzón-Toro, 2015; Glubb et al., 2017; Vijayakrishnan et al., 2017; Moreno-Moral et al., 2018; Cochran et al., 2020	PLINK (https://zzz.bwh.harvard.edu/plink/)	Linear regression model	EOC, sMTC and PTC, leukemia	Supervised
miRNA-disease associations	Liu et al., 2019	NBMDA	Gaussian interaction profile kernel similarity/KNN	Esophageal, breast, and colon neoplasms	Supervised
Learning and characterization of chromatin states	Bien et al., 2017	ChromHMM http://compbio.mit.edu/ChromHMM/	HMM	CRC	Supervised
Analysis of high-throughput sequencing data (ChIP-seq, RNA-seq, MNase-seq)	Han et al., 2016	DeepTools https://deeptools.readthedocs.io/en/develop/	k-means clustering	AML	Unsupervised

AML, acute myeloid leukemia; CRC, colorectal cancer; eQTL, expression quantitative trait loci; EOC, epithelial ovarian cancer; HMM, Hidden Markov Model; NB, negative binomial; KNN, k-nearest neighborhood, PICS, Probabilistic identification of causal SNPs; PTC, papillary thyroid carcinoma; RF, Random forest; ReMM, Regulatory Mendelian mutation; RVF, Rift valley fever; sMTC, sporadic medullar thyroid carcinoma; SVM, support-vector machine; SVR, support-vector regression.