{"id":1819,"date":"2025-02-04T01:48:08","date_gmt":"2025-02-04T01:48:08","guid":{"rendered":"https:\/\/sites.rutgers.edu\/precision-medicine\/?p=1819"},"modified":"2025-04-09T18:26:02","modified_gmt":"2025-04-09T18:26:02","slug":"book-chapter-applying-ai-ml-for-analyzing-gene-expression-patterns","status":"publish","type":"post","link":"https:\/\/sites.rutgers.edu\/precision-medicine\/book-chapter-applying-ai-ml-for-analyzing-gene-expression-patterns\/","title":{"rendered":"Applying AI\/ML for Analyzing Gene Expression Patterns"},"content":{"rendered":"

Our latest chapter\/article published in book i.e., Gene Expression Analysis<\/a>, part of the book series: Methods in Molecular Biology<\/a>, edited by Drs. Nalini Raghavachari and Natalia Garcia-Reyer.<\/p>\n

Applying AI\/ML for Analyzing Gene Expression Patterns<\/a><\/strong><\/p>\n

Artificial intelligence (AI) and machine learning (ML) have advanced in several areas and fields of life; however, its progress in the field of genomics is not matching the levels others have achieved. Challenges include but are not limited to the handling and analysis of high volumes of complex genomic data, and the expertise needed to implement and execute AI\/ML approaches. In this chapter, we highlight the importance of transcriptomics, and RNA-seq driven gene expression data exploration to discover novel biomarkers and predict rare, common, and complex diseases. We discuss relevant high volume sequence data generated in the recent past and its availability through various channels, development of orthodox bioinformatics tools and technologies to investigate significantly expressed and abundantly enriched genes, and the implementation of cutting-edge AI\/ML approaches to observe disease specific patterns. Current challenges include but are not limited to the acceptance of AI\/ML in the scientific research and clinical environments, especially in providing personalized diagnoses and treatments. Reasons include unavailability of user-friendly AI\/ML applications and reproducible results. Addressing these issues, we discuss our recently developed Findable, Accessible, Intelligent, and Reproducible (FAIR) solutions, designed for the users with and without computational background to discover biomarkers and predict diseases with high accuracy. We strongly believe that the rightful application of AI\/ML techniques has the potential to open avenues for broader research, ultimately leading to personalized interventions and novel treatment targets. Its widespread application will contribute to the public health at large in the United States and around the globe.<\/p>\n

Publication\/Citation:<\/strong><\/p>\n