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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Paperback. Condición: new. Paperback. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9783659469923
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 56 pp. Englisch. Nº de ref. del artículo: 9783659469923
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Paperback. Condición: new. Paperback. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Nº de ref. del artículo: 9783659469923
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