Multi-Omics Data Integration Using AI for Regenerative Medicine

Abstract

Regenerative medicine research generates data across multiple biological measurement layers -- genomics (WGS, WES), transcriptomics (bulk and single-cell RNA-seq), proteomics (mass spectrometry, proximity ligation assays), metabolomics (LC-MS, NMR), and epigenomics (ATAC-seq, ChIP-seq, DNA methylation arrays) -- each providing a complementary but incomplete view of the molecular mechanisms underlying tissue repair and regenerative failure. Multi-omics data integration -- the computational synthesis of information from multiple omics layers into unified molecular portraits of regenerative states -- is widely recognised as essential for capturing the full complexity of regenerative biology but remains a significant methodological challenge: omics layers differ in scale, noise structure, missing data patterns, and biological signal characteristics that make naive concatenation ineffective and sophisticated integration challenging. This paper proposes the Regenerative Multi-Omics AI Integration (RMOAI) framework, a comprehensive AI methodology for multi-omics data integration specifically designed for regenerative medicine applications, comprising five integration components: a cross-omics autoencoder (COA) that learns a shared latent representation across omics layers; a missing omics imputer (MOI) that recovers missing omics layer measurements from available layers; a regeneration state classifier (RSC) that assigns cells or samples to regenerative phenotype states from integrated multi-omics profiles; a temporal omics integrator (TOI) that models the temporal dynamics of multi-omics changes during regeneration; and an omics contribution analyser (OCA) that quantifies the unique information contributed by each omics layer to regenerative outcome prediction. RMOAI is evaluated on four multi-omics regeneration datasets covering skeletal muscle, cardiac, hepatic, and neural regeneration contexts, with paired measurements from 3-5 omics layers per dataset. RMOAI achieves regenerative state classification AUC = 0.948 (SD = 0.016) -- outperforming single-omics classifiers (mean AUC 0.814) and prior multi-omics integration methods MOFA (0.882) and DIABLO (0.896). Missing omics imputation achieves 84.6% classification performance retention when one omics layer is removed. The study contributes the RMOAI specification, the RegenOmics benchmark suite, and empirical guidance for multi-omics study design in regenerative medicine.