Main subject: Computational Biology

Related subjects: Bioinformatics, Artificial Intelligence, Algorithms, Genomics, Transcriptomics, Proteomics, Metabolomics, Epigenomics, Microbiomics

Aims and motivations
Computational models are transforming biology and medicine, providing powerful tools to interpret the massive datasets generated by modern technologies. This series offers a comprehensive view of emerging trends in computational biology for graduate students and professionals in computer science, engineering, biology, and medicine.
Each volume will explore how computational intelligence, machine learning, and deep learning are applied to large-scale omics data—genomics, transcriptomics, proteomics, metabolomics, epigenomics, and microbiomics—alongside clinical and lifestyle factors. The books highlight how AI identifies patterns, correlations, and biomarkers within complex datasets, deepening our understanding of biological processes, disease mechanisms, and therapeutic targets.
The series examines the role of AI in automating data analysis and visualization, integrating multi-omics data, and advancing precision medicine through individualized diagnosis, treatment, and monitoring. It also explores AI’s impact on biomarker discovery, drug target identification, and clinical trial optimization—reducing cost and accelerating innovation.
A strong emphasis is placed on open-source tools, reproducibility, and ethical considerations to ensure transparency, collaboration, and responsible AI adoption.

Key Topics Include:

• RNA and protein structure prediction
• Multi-omics integration and analysis
• Metabolic pathway analysis
• Gene regulatory network reconstruction
• Molecular evolution and phylogenetics
• Sequence alignment and analysis
• Large-scale biological data visualization
• Treatment and drug optimization
• Modeling and simulation of biological systems
• Complexity, robustness, and evolvability in biological networks