ML for Target Identification & Validation in Drug Discovery - DURGA, PUTTA;VENKATESWARA RAO, SADHU
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Machine Learning Approaches for Target Identification and Validation in Drug Discovery examines the transformative role of machine learning (ML) in enhancing the drug discovery process. The introduction highlights the importance of accurate target identification and validation, while subsequent sections delve into various ML algorithms for predicting potential drug targets based on biological data. Gene prioritization methods are discussed, showcasing how ML can effectively rank disease-associated genes. Additionally, the integration of ML with knowledge graphs is explored, illustrating how…mehr

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Produktbeschreibung
Machine Learning Approaches for Target Identification and Validation in Drug Discovery examines the transformative role of machine learning (ML) in enhancing the drug discovery process. The introduction highlights the importance of accurate target identification and validation, while subsequent sections delve into various ML algorithms for predicting potential drug targets based on biological data. Gene prioritization methods are discussed, showcasing how ML can effectively rank disease-associated genes. Additionally, the integration of ML with knowledge graphs is explored, illustrating how these tools enhance data connectivity and decision-making. Finally, the importance of information extraction through data mining and natural language processing is addressed, illustrating how these approaches help researchers extract valuable insights from large datasets, thereby advancing the field of drug discovery.
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Autorenporträt
Dr. Putta Durga holds 4 patents, 12 Scopus/SCI indexed articles, Raman Research Award 4 times, Best Research Paper Presentation Award 2 times and 3 Springer book chapters. Currently, she is working as Assoc. Professor in the Department of Computer Science and Engineering (CSE) at the NRI Institute of Technology, Vijayawada, Andhra Pradesh, India.