Machine Learning and Systems Biology in Genomics and Health
Dive into the transformative intersection of artificial intelligence, genomics, and systems biology with Machine Learning and Systems Biology in Genomics and Health, edited by Shailza Singh and published by Springer Singapore in February 2022. This groundbreaking volume (236 pages) is essential for researchers, geneticists, clinical scientists, and bioinformatics professionals working across precision medicine, computational biology, and health informatics.
📚 What You’ll Learn:
-
How machine learning frameworks integrate multi-omics data such as genomics, transcriptomics, and proteomics for robust clinical diagnostics, disease classification, and survival prediction strategy.
-
Applications across infectious disease, metabolic disorders, oncology, and cardiovascular systems, combining systems biology and AI-driven biomarker discovery for precision therapies.
-
Case study protocols including neural network modeling for disease detection, regulatory network reconstruction, non-coding RNA biomarker identification, biomedical image analysis, and AI in cardiovascular genomics.
The book highlights innovative uses of systems biology frameworks to model genotype–phenotype interactions and identify drug targets and diagnostic markers via computational pipelines.
✨ Special Features:
-
Edited by Dr. Shailza Singh, a renowned bioinformatics scientist and head of HPC operations at National Centre for Cell Science, India – recognized with national and international honors in computational biology and systems modeling.
-
A concise, yet comprehensive volume (~236 pages) that maintains clarity without sacrificing depth—ideal for graduate-level instruction or focused professional reference.
-
Published by Springer Nature, known for quality academic texts in life sciences and informatics.
