From:  Explainable AI for multi-omics in precision medicine: a systematic review

 Common types of omics data and their biological significance.

Omics layerPrimary data typeBiological significanceKey characteristicsCommon platforms
GenomicsDNA sequences, variants (SNPs: single nucleotide polymorphisms, CNVs: copy number variations, mutations)Germline and somatic alterations; disease susceptibility; drug metabolism; heritable traitsStatic across cell types; high dimensionality (millions of variants); discrete, sparse dataWhole-genome sequencing, whole-exome sequencing, SNP arrays
TranscriptomicsRNA abundance (mRNA, ncRNA)Gene expression patterns; regulatory networks; cellular state; functional activityDynamic, cell-type specific; count-based, overdispersed; high dimensionality (thousands of transcripts)RNA-seq, microarrays
ProteomicsProtein abundance, post-translational modificationsFunctional effectors; signaling pathways; drug targets; direct phenotype mediatorsModerate dimensionality; continuous intensity data; partial correlation with transcriptomicsMass spectrometry, antibody-based assays
MetabolomicsMetabolite concentrations (small molecules)Metabolic state; physiological readout; closest to phenotype; biomarker-richLower dimensionality (hundreds to thousands); high chemical diversity; dynamic rangeNMR, mass spectrometry
EpigenomicsDNA methylation, histone modifications, chromatin accessibilityGene regulation; environmental response; cell identity; tissue specificityTissue-specific; spatial dependencies; binary or continuous signalsBisulfite sequencing, ChIP-seq, ATAC-seq
MicrobiomicsMicrobial taxonomic and functional profilesHost-microbe interactions; immune modulation; drug metabolism; disease associationsCompositional data; high sparsity; cross-sectional or longitudinal16S rRNA sequencing, metagenomic sequencing

Sources: Adapted from [14, 39, 40].