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Neuroinformatics for Drug Discovery: Data-Driven Approaches to Understand and Treat Brain Diseases
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- FormatePub
- ISBN8232047948
- EAN9798232047948
- Date de parution30/09/2025
- Protection num.pas de protection
- Infos supplémentairesepub
- ÉditeurDraft2Digital
Résumé
Neuroinformatics for Drug Discovery: Data-Driven Approaches to Understand and Treat Brain Diseases is an exciting, student-written guide that explores how computational science, bioinformatics, and big data analytics are revolutionizing the discovery of new brain therapies. Authored by Kompala Jhansi, this book integrates neuroscience ??, machine learning ??, and systems biology ?? in a way that makes even the most advanced concepts both accessible and inspiring.
Written from the perspective of a student researcher, it spans 12 comprehensive chapters covering essential tools, computational pipelines, and real-world case studies. Readers will explore technical areas such as multi-omics data integration (genomics, proteomics, neuroimaging), AI-driven predictive modeling, virtual screening for CNS-active compounds, and CRISPR-based genome editing ??. Designed for undergraduates, graduate students, and early-career researchers, the book highlights how data-driven strategies are accelerating translational research for disorders like Alzheimer's, Parkinson's, ALS, and Huntington's.? Key areas for researchers & scientists: ?? Multi-scale brain data integration ? combining omics, imaging, and clinical datasets ?? Machine learning models ? diagnostics, biomarker discovery, and drug design ?? Network biology approaches ? drug repurposing & target prioritization ?? Clinical trial stratification ? precision medicine through data clustering ?? CRISPR & genome editing ? next-gen therapeutic strategies for CNS diseases ?? Ethics & data governance ? ensuring patient data security and trustworthy AI ?? More than just a technical handbook, this book also reflects a student's research journey through collaborative projects and internships-offering inspiration for young scientists to develop explainable, trustworthy AI systems that translate into real-world therapeutic impact.
By the end, readers will gain not only cutting-edge knowledge but also the motivation to contribute toward a future where neurodegenerative disorders are decoded and treated through data-driven innovation.
Written from the perspective of a student researcher, it spans 12 comprehensive chapters covering essential tools, computational pipelines, and real-world case studies. Readers will explore technical areas such as multi-omics data integration (genomics, proteomics, neuroimaging), AI-driven predictive modeling, virtual screening for CNS-active compounds, and CRISPR-based genome editing ??. Designed for undergraduates, graduate students, and early-career researchers, the book highlights how data-driven strategies are accelerating translational research for disorders like Alzheimer's, Parkinson's, ALS, and Huntington's.? Key areas for researchers & scientists: ?? Multi-scale brain data integration ? combining omics, imaging, and clinical datasets ?? Machine learning models ? diagnostics, biomarker discovery, and drug design ?? Network biology approaches ? drug repurposing & target prioritization ?? Clinical trial stratification ? precision medicine through data clustering ?? CRISPR & genome editing ? next-gen therapeutic strategies for CNS diseases ?? Ethics & data governance ? ensuring patient data security and trustworthy AI ?? More than just a technical handbook, this book also reflects a student's research journey through collaborative projects and internships-offering inspiration for young scientists to develop explainable, trustworthy AI systems that translate into real-world therapeutic impact.
By the end, readers will gain not only cutting-edge knowledge but also the motivation to contribute toward a future where neurodegenerative disorders are decoded and treated through data-driven innovation.
