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AI-Powered Integrative Omics Analysis for Predicting Therapeutic Outcomes in Lung Diseases

AI-Powered Integrative Omics Analysis for Predicting Therapeutic Outcomes in Lung Diseases
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Author(s): Poonam K. Verma (Graphic Era Hill University, India)and Nisha Chandran (Graphic Era Hill University, India)
Copyright: 2026
Pages: 20
Source title: Next-Generation Bioinformatics for Pulmonary Disease Research
Source Author(s)/Editor(s): Devvret Verma (Graphic Era University, India), Debasis Mitra (Graphic Era University, India), Bhavya Mudgal (Graphic Era University, India), Suraj Vitthaloo Atram (The University of Sheffield, UK)and Rokayya Sami (Taif University, Saudi Arabia)
DOI: 10.4018/979-8-3373-4923-7.ch004

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Abstract

Lung diseases, encompassing conditions such as chronic obstructive pulmonary disease (COPD), asthma, lung cancer, and interstitial lung diseases, present complex clinical challenges due to heterogeneous pathophysiological mechanisms. Integrative omics approaches—genomics, transcriptomics, proteomics, metabolomics, and epigenomics—have revealed multilayered insights into disease etiology and progression. However, translating this multidimensional data into predictive models for therapeutic outcomes remains elusive. Artificial Intelligence (AI), particularly through deep learning and integrative data frameworks, has emerged as a powerful paradigm for unifying disparate omics layers to predict patient-specific treatment responses. This chapter delineates the architecture and utility of AI-powered integrative omics models in lung diseases, highlighting advances in data fusion, interpretability, and precision medicine. We conclude by discussing current limitations, ethical considerations, and future directions for scalable deployment in clinical settings.

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