Robust and Interpretable Multi-modal AI/ML for Precision Medicine

Univ Of North Carolina Chapel Hill, Chapel Hill NC

Investigators

Abstract

Project Summary Biomedical research increasingly leverages multi-modal data, including genomic, multi-omics, medical imaging, and clinical narratives, to advance precision medicine. The integration of these diverse data types is significant because it enables a comprehensive understanding of complex diseases, such as cancer and Alzheimer's disease, by combining the genetic, physiological, and clinical aspects of patient health. Such rich multi-modal data offer unparalleled opportunities for advancing precision medicine and improving health outcomes. However, the field faces several daunting challenges: (1) Complex and Heterogeneous Modalities: The integration of complex data types, such as high-dimensional genomic data and high-resolution medical imaging, is challenging due to significant noise and data misalignment, necessitating sophisticated data imputation and denoising methods. Moreover, significant multi-modal interference and suboptimal integration performance arise from substantial data heterogeneities, which is one of the key limitations of existing multimodal methods. It necessitates innovative use of advanced regularized optimization and network architecture designs to alleviate such issues. (2) Reliance on Single-Modality and Incomplete Multi-Modal Data: Traditional and emerging multi-modal frameworks often fail to effectively synthesize complex datasets. Single-modality approaches miss interconnected insights, while multi-modal methods struggle with cross-modal interactions and optimal performance, especially lacking the ability to cope with incomplete or missing multi-modal data that are ubiquitous in practice. (3) lnterpretability: Existing multi-modal models often neglect essential cross-modal interactions and rely on posthoc explanations, lacking mechanistic interpretability. To bridge these significant gaps via a novel multi-modal learning framework, we propose the following aims. Aim 1: To illuminate the black box of biomedical multi-modal data fusion: Toward effective and interpretable multi-modal biomedical Al. Aim 2: To address incomplete biomedical multi-modal data via retrieval-based methods: Toward robust multi-modal clinical classification and prediction. Aim 3: To bridge missing modalities in multi-modal biomedical data via optimization-based methods: Toward flexible integration with arbitrary modality combinations. Aim 4: To assess and validate the proposed multi-modal learning framework through integrative analysis of ADNI data for Alzheimer's Disease and TCGA data for cancer. Throughout the experiments, to verify our proposed methods, we will use performance metrics like ACC, Macro-F1, ARI, c-index, and AUROC, evaluating them on datasets including ADNI, Patch-seq GABAergic neuron, multi-omeATAC + gene expression BMMC, TCGA, ABIDE, and Duke Breast. The proposed methods are expected to accelerate biomedical research and improve healthcare by enhancing multi-modal data integration, overcoming analysis challenges, and providing valuable insights into the molecular and physiological processes underlying complex diseases, leading to more effective and tailored therapeutic strategies.