Neuroinformatics for Drug Discovery: Data-Driven Approaches to Understand and Treat Brain Diseases (eBook, ePUB)
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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 tool...
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:
💡 More than just a technical handbook, this book also reflects a student's research journey through collaborative projects and internshipsoffering 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 internshipsoffering 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.
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