Machine Learning and Mathematical Models in Evolutionary Biology
Insights, Innovations, and Applications
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Beschreibung
The book discusses the advantages of using mathematical modeling and machine learning in the context of the evolutionary biology domain to gain knowledge and develop further. It discusses the background ideas regarding evolutionary theory, population behavior and computation, and advances to the current topics of evolutionary algorithms, nonlinear modeling, and data-driven analysis.
The volume proposes the application of theoretical models and clever algorithms to the analysis of complex biological systems, ecological interactions, and real-world problems in health, genomics, and engineering. Combining classical theories with some new computational tools, the book proves that machine learning is able to make predictions more accurate and reduce some parameters and process large amounts of data more efficiently in biological studies. It also covers disease modelling, genomic prediction, tumor growth and socio-environmental dynamics and is also interdisciplinary in exploring network systems and biomedical engineering. On the whole, the book offers an integrative and prospective view to scientists and professionals regarding the innovative aspects at the interplay of biology, mathematics, and artificial intelligence and highlights the future of evolutionary science and intelligent models.
The book discusses the advantages of using mathematical modeling and machine learning in the context of the evolutionary biology domain to gain knowledge and develop further. It discusses the background ideas regarding evolutionary theory, population behavior and computation, and advances to the current topics of evolutionary algorithms, nonlinear modeling, and data-driven analysis.
The volume proposes the application of theoretical models and clever algorithms to the analysis of complex biological systems, ecological interactions, and real-world problems in health, genomics, and engineering. Combining classical theories with some new computational tools, the book proves that machine learning is able to make predictions more accurate and reduce some parameters and process large amounts of data more efficiently in biological studies. It also covers disease modelling, genomic prediction, tumor growth and socio-environmental dynamics and is also interdisciplinary in exploring network systems and biomedical engineering. On the whole, the book offers an integrative and prospective view to scientists and professionals regarding the innovative aspects at the interplay of biology, mathematics, and artificial intelligence and highlights the future of evolutionary science and intelligent models.
Integrates evolutionary biology with AI-driven predictive modeling across diverse real-world systems Combines theoretical rigor with case-based applications in health, ecology, and engineering Bridges biological and computational sciences using hybrid mathematical–machine learning frameworks
Autor*in
Satyvir Singh
Themen in »Machine Learning and Mathematical Models in Evolutionary Biology«
Evolutionary Biology Mathematical Modeling Evolutionary Algorithms Computational Biology Population Dynamics Dynamical Systems Genomic Prediction Artificial Intelligence in Biology Stochastic Modeling Optimization Techniques Predator–Prey Models Epidemiological Modeling Systems Biology Data-Driven Modeling Hybrid AI Models