Data-driven decision-making for precision diagnosis of digestive diseases

Table 2 Data-driven precision diagnosis in digestive diseases based on genomics

First author, year	Disease	n	Data source and specific task	ML method	Diagnostic performance	Refs.
Ichikawa, 2017	GC	207	Tumor tissue WGS data; actionable gene-based classification	Hierarchical clustering	–	[89]
Imperiale, 2014	CRC	9989	Multitarget stool DNA testing data; multimarker-based classification	LR	Sensitivity: 92.3% Specificity: 84.6%	[92]
Luo, 2020	CRC	1822	Circulating tumor DNA methylation markers; multimarker-based classification	LASSO/RF	AUC: 0.870	[93]
Romagnoni, 2019	Crohn disease	5277	Genome-wide genotyping data; genetic variant-based classification	Penalized LR/GBT/ANN	AUC: 0.802	[94]
Chung, 2023	CMMRD	639	Low-pass genomic instability characterization (LOGIC) assay; classification based on genomic microsatellite signature	LR	Sensitivity: 100%	[95]
Zuo, 2022	PEAC	86	Tumor tissue WES and targeted bisulfite sequencing data; DNA methylation-based classification	RF/LASSO/ SVM/ XGBoost	AUC: 0.900–1.000	[96]
Wan, 2019	CRC	817	WGS data of plasma cfDNA; classification based on genetic features	PCA/SVM/ LR	AUC: 0.920 (95% CI, 0.910–0.930)	[97]
Cakmak, 2023	CRC	115	SNP profiles of immune phenotypes; prediction based on SNPs	LR/RF/SVM/KNN	AUC: 0.960	[98]
Guo, 2023	CRC	173	Tissue RNA-seq data; WGCNA, classification based on key hub genes	LASSO	AUC: 0.821–1.000	[99]
Killcoyne, 2020	EC	412	Shallow WGS data; classification based on genomic copy numbers	Elastic-net regression	Sensitivity: 72.0% Specificity: 82.0%	[100]

Full names of abbreviations are given in the Abbreviations section of the manuscript

ISSN: 1475-925X