Ahmed Zeeshan
Department of Medicine, Division of Cardiovascular Disease and Hypertension, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA.
Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers Health, New Brunswick, NJ, USA.
Methods Mol Biol. 2025;2880:319-330. doi: 10.1007/978-1-0716-4276-4_16.
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.
人工智能（AI）和机器学习（ML）在生活的多个领域都取得了进展；然而，其在基因组学领域的进展却未能达到其他领域所取得的水平。挑战包括但不限于处理和分析大量复杂的基因组数据，以及实施和执行人工智能/机器学习方法所需的专业知识。在本章中，我们强调转录组学以及由RNA测序驱动的基因表达数据探索对于发现新型生物标志物和预测罕见病、常见疾病及复杂疾病的重要性。我们讨论了近期生成的相关大量序列数据及其通过各种渠道的可获取性，用于研究显著表达和大量富集基因的传统生物信息学工具和技术的发展，以及用于观察疾病特定模式的前沿人工智能/机器学习方法的实施。当前的挑战包括但不限于在科研和临床环境中接受人工智能/机器学习，特别是在提供个性化诊断和治疗方面。原因包括缺乏用户友好的人工智能/机器学习应用程序以及可重复的结果。针对这些问题，我们讨论了我们最近开发的可查找、可访问、智能且可重复（FAIR）的解决方案，该方案旨在为有和没有计算背景的用户设计，以高精度发现生物标志物并预测疾病。我们坚信，正确应用人工智能/机器学习技术有潜力为更广泛的研究开辟道路，最终实现个性化干预和新的治疗靶点。其广泛应用将对美国乃至全球的公共卫生做出贡献。
