Machine Learning and Computational Studies in Cancer

An Interdisciplinary Approach
Erscheinungsjahr 2027
ISBN: 978-3-032-38792-9
Verlag: Springer

Buch, Englisch, 311 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: Interdisciplinary Cancer Research

“Machine Learning and Computational Studies in Cancer: An Interdisciplinary Approach” is the thirtieth volume of the “Interdisciplinary Cancer Research” series, and a comprehensive volume on machine learning and computational studies in cancer research.

The volume explores the transformative role of artificial intelligence, machine learning, and computational methods in advancing cancer research and precision oncology. It brings together contributions on computational diagnostics and biomarkers, predictive modeling, systems and quantitative approaches to understanding cancer complexity, multi-omics and bioinformatics analyses, and AI-enabled innovations in cancer research.

This is the main concept of the Cancer Immunology Project (CIP), which is a part of the Universal Scientific Education and Research Network (USERN). This interdisciplinary book will be of special value for those who wish to have an update on machine learning and computational studies in cancer.

Zielgruppe

Research

Autoren/Hrsg.

Rezaei, Nima

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Klinische und Innere Medizin Onkologie, Krebsforschung

Inhaltsverzeichnis

The Role of Artificial Intelligence in Detecting Cancer Stem Cells.- Computational Cancer Biomarkers: An Interdisciplinary Approach.- Modeling Uncertainty of Cancer: A Fuzzy-Fractional Perspective on Tumor-Immune-Chemotherapy Dynamics.- Exploring Cancer Metabolism through Multi-Omics Approach: From Biomarkers to Therapeutic Targets.- Interobserver Variability in Histopathological Cancer Grading: Causes, Consequences, and Computational Solutions.- Non-Invasive Cancer Prediction Models: The Combined Role of Salivary Matrix Metalloproteinases, Clinicopathological Features, Oral Condition, and Sociodemographic Factors.- Leveraging Artificial Intelligence in Chordoma: Interdisciplinary Strategies to Advance Research and Clinical Management.- Computational Approaches to Untangle the Interconnected Roles of Nucleolin and BRCA1 in Breast Cancer.- Genomics, AI, and Targeted Therapy in Thyroid Cancer.- Unraveling HPV-Driven Cancer Mechanisms through RNA-Seq and Bioinformatics.- Obesity, Western-Diets, and Immune Checkpoint Inhibitors: Bioinformatic Insights into Hypothalamic Neuro–Immune Crosstalk.

Über Autor(innen)

Professor Nima Rezaei gained his medical degree (MD) from Tehran University of Medical Sciences and subsequently obtained an MSc in Molecular and Genetic Medicine and a PhD in Clinical Immunology and Human Genetics from the University of Sheffield, UK. He also spent a short-term fellowship of Pediatric Clinical Immunology and Bone Marrow Transplantation in the Newcastle General Hospital. Professor Rezaei is now the Full Professor of Immunology and Vice Dean of Research and Technologies, School of Medicine, Tehran University of Medical Sciences, and the co-founder and Head of the Research Center for Immunodeficiencies. He is also the Founder of Universal Scientific Education and Research Network (USERN). Prof. Rezaei has already been the Director of more than two hundred research projects and has designed and participated in several international collaborative projects. Prof. Rezaei is the editor, editorial assistant, or editorial board member of more than fifty international journals. He has edited more than one hundred international books, has presented more than a thousand lectures/posters in congresses/meetings, and has published more than 1,700 scientific papers in the international journals.