DESCRIPTION:
The "Artificial Intelligence and Bioinformatics in Cancer: An Interdisciplinary Approach" is the eighteenth volume of the "Interdisciplinary Cancer Research" series, publishes comprehensive volume on the advances of machine learning and bioinformatics in cancer. The volume starts with a chapter on application of artificial intelligence for early diagnosis of cancer. Then digital health technologies in cancer care and research is discussed. Unveiling cancer complexity: machine learning insights into multi-omics data and the role of integrated bioinformatics in cancer research are also discussed. In silico and biophysical approaches in cancer research and in silico methods and targeted receptors used in cancer studies are explained in the following chapters.
The modeling uncertain growth and diffusion in cancer tumors with heterogeneous cell mutations, imaging tumor metabolism and its heterogeneity with special focus on radiomics and artificial intelligence are also discussed. Mathematical modeling of cancer tumor dynamics as well as recent advances in artificial intelligence for cancer treatment are presented, while signature-based drug repositioning for drug discovery employing machine learning tools is also discussed. After a chapter on mathematical analysis of cancer-tumor models, the subsequent chapters discuss on the role of artificial intelligence in colorectal cancer, breast cancer, lung cancer, brain tumor, and cervical cancer. This is the main concept of Cancer Immunology Project (CIP), which is a part of Universal Scientific Education and Research Network (USERN). This interdisciplinary book will be of special value for oncologists who wish to have an update on application of artificial intelligence in diagnosis and treatment of cancers.
CONTENTS:
Digital Pathology and Artificial Intelligence for Early Diagnosis of Pediatric Solid Tumors: Implication for Improved Healthcare Strategies
Digital Health Technologies in Cancer Care and Research
Unveiling Cancer Complexity: Machine Learning Insights into Multi-omics Data
The Role of Integrated Bioinformatics in Cancer Research: Transforming Genomic Insights into Precision Medicine
In Silico and Biophysical Techniques in Anticancer Drug Discovery Research
In Silico Methods and Targeted Receptors Used in Cancer Studies
Modeling Uncertain Growth and Diffusion in Cancer Tumors with Heterogeneous Cell Mutations
Imaging Tumor Metabolism and Its Heterogeneity: Special Focus on Radiomics and AI
Mathematical Modeling of Cancer Tumor Dynamics with Multiple Fuzzification Approaches in Fractional Environment
Is Cancer Our Equal or Our Better? Artificial Intelligence in Cancer Drug Discovery
Recent Advances in Artificial Intelligence and Cancer Treatment
Signature-Based Drug Repositioning: Tackling Speeding Up Drug Discovery of Anticancer Drugs Employing Recently Developed Machine Learning Tools
Mathematical Analysis of Cancer-Tumor Models with Variable Depression Effects and Integrated Treatment Strategies
Emerging Role of Artificial Intelligence in Colorectal Cancer: Screening and Diagnosis
Measuring the Performance of Supervised Machine Learning Approaches Using Cancer Data
VRTumor: Integrating AI-Based Segmentation with Virtual Reality for Precise Tumor Analysis
Artificial Intelligence Applications to Detect Pediatric Brain Tumor Biomarkers
