Deep Learning in Genetics and Genomics 1st Edition

Capture the future of biological discovery with Deep Learning in Genetics and Genomics, 1st Edition by Khalid Raza. This authoritative guide bridges cutting-edge machine learning with real-world genomic challenges, offering readers a clear pathway from foundational concepts to practical applications in research and clinical settings.

Inside, you’ll find lucid explanations of neural networks, convolutional and recurrent architectures, and emerging transformer models tailored to genetic and genomic data. Case studies demonstrate variant calling, gene expression analysis, epigenomics, and population genetics using deep learning pipelines. Each chapter pairs theory with reproducible workflows and best practices for preprocessing, model selection, and interpretation—designed for bioinformaticians, computational biologists, clinicians, and advanced students.

What sets this volume apart is its pragmatic focus: learn how deep learning accelerates biomarker discovery, improves diagnostic accuracy, and supports precision medicine initiatives across laboratories and hospitals worldwide. Readers in North America, Europe, Asia, and beyond will appreciate its relevance to diverse datasets and regulatory environments.

Practical, rigorous, and forward-looking, Khalid Raza’s 1st Edition equips you to harness AI responsibly in genomics research and translational projects. Whether you’re building production pipelines, preparing grant proposals, or advancing academic study, this book is an essential resource for staying competitive in the rapidly evolving fields of genetics and bioinformatics.

Ready to elevate your genomics expertise? Add Deep Learning in Genetics and Genomics, 1st Edition to your library and transform data into discovery.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.