Institutional Publications

Permanent URI for this collectionhttps://ndkr-library.nipgr.ac.in/handle/123456789/11

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Subject: search.filters.subject.Artificial intelligence

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    The intersection of AI and genomics in health and disease: Advancements and applications
    (Elsevier B.V., 2026) Kaushik, Love; Vivek, A T; Arora, Simran; Hamid, Fiza; Mukherjee, Kanka; Bisht, Niyati; Chaudhary, Sakshi; Shukla, Jagriti; Nawani, Sakshi; Kumar, Shailesh
    AI and genomics are revolutionizing precision medicine by using machine learning (ML) to analyze large-scale next-generation sequencing (NGS) data, identifying genetic mutations and biomarkers for personalized therapies. In practice, this accelerates drug discovery and enhances variant detection, while in cancer genomics, AI enables early detection via liquid biopsies and refines treatment by integrating multi-omics data to improve therapeutic precision. However, challenges such as data biases in underrepresented populations, limited model interpretability, and ethical concerns regarding privacy and algorithmic inequity hinder clinical adoption and demand robust governance. Efforts to diversify datasets also face standardization hurdles, although explainable AI and federated learning provide promising solutions for improving transparency and privacy. In this chapter, we discuss the role of AI in advancing genomics from diagnostics to novel therapies and emphasize the need for equitable frameworks to ensure responsible implementation, thereby paving the way for breakthroughs in personalized medicine.
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    Next-generation translational genomics for developing future crops
    (Springer Nature Publishing AG, 2025) Basu, Udita; Parida, Swarup K.
    Advancements in translational genomics have revolutionized crop breeding, driving us from traditional breeding methods towards next-generation strategies that integrate genomic, transcriptomic, and phenotypic data to expedite crop improvement. There has been a shift from single genomes to pan-genomes, which better capture intraspecific diversity, and from bulk transcriptome analyses to single-cell transcriptomics, enabling cell-specific insights into gene regulation and functional genomics. Both high throughput genopyting and phenotyping approaches are now possible due to rapid technological advancement in the field of translational genomics. Large-scale phenotyping data from multi-environment field trials is now possible due to AI-enabled digital and drone-based scanning. In the era of artificial intelligence and machine learning we have developed flexible models to handle complex genetic architecture of trait regulation using various tools and approaches. These genetic and genomic resources are the foundation for generating novel, adaptable, and high-yielding varieties, accelerating trait discovery and mapping. This review explores the comprehensive landscape of modern translational genomics, highlighting key shifts and innovations that enhance our capacity to address agricultural challenges. Integrative pipelines that unify these next-generation approaches could facilitate faster, more precise, and sustainable crop improvement, ultimately meeting the growing demands for future-ready crops.
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    AI & ethics: charting a responsible future
    (NATL INST SCIENCE COMMUNICATION-NISCAIR, 2024) Yadav, Gitanjali; Munshi, Angad; Kumari, Renu; Singh, Dhananjay; Kumari, Neeraj; Munshi, Usha Mujoo
    Artificial intelligence (AI) is rapidly transforming the world, but its development and deployment raise critical ethical questions. This paper explores the key themes that emerged from a national conclave on AI and Ethics in India, bringing together industry and academic leaders. We examine the potential of AI for various sectors, with a thematic case study for the Genome Biology sector, alongside concerns about bias, privacy, and accountability. AI development and use, while underscoring the need for an ethical framework to guide its evolution, emphasizes the need for collaboration between academia and industry to develop ethical frameworks and translate principles into practical applications. In summary, ethical AI may serve as a moral framework of AI technologies to ensure that our technological capability aligns with fundamental societal values and human dignity on the road to progress. This framework by definition would not be a static set of commandments but a dynamic constantly evolving idea about the use of technologies like AI.