Browsing by Author "Chaudhary, Sakshi"

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    PFGPred: A stack ensemble classifier for the identification of fusion genes in plants
    (Oxford University Press, 2026) Hamid, Fiza; Mukherjee, Kanka; Chaudhary, Sakshi; Kaushik, Love; Kumar, Shailesh
    Fusion genes play crucial roles in plant biological processes but remain far less explored than their human counterparts, largely due to limited validated datasets and the absence of plant-specific prediction tools. Existing approaches often produce high false-positive rates, restricting reliable discovery. To address this gap, we developed Plant Fusion Gene Predictor (PFGPred), an ensemble machine learning framework that integrates Random Forest, XGBoost, and long short-term memory (LSTM) models into a meta-classifier for accurate identification of true and false fusion genes from RNA-Seq data. PFGPred was trained on a high-confidence dataset of fusion genes validated by both RNA-Seq and whole-genome sequencing from Arabidopsis thaliana, Oryza sativa, Triticum aestivum, and Zea mays, to predict and rank candidate fusion genes for future functional validation. It outperformed individual baseline models, achieving accuracies of 0.97 on training data and 0.77 on independent test data. When evaluated on human datasets, it achieved 0.71 accuracy with lower sensitivity, reflecting biological differences between plant and human fusion events. Comparative analyses confirmed that PFGPred reliably identifies validated fusions, demonstrating its utility as a cost-effective, plant-specific prediction tool for high-throughput fusion gene screening and functional genomics research. It is freely available as a web server at http://www.nipgr.ac.in/PFGPred.
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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.