Computational and Analytic Methods in Biological Sciences

Bioinformatics with Machine Learning and Mathematical Modelling

Akshara Makrariya, Brajesh Kumar Jha, Rabia Musheer, Anant Kant Shukla, Amrita Jha, Parvaiz Ahmad Naik

• Publisher: River Publishers
• Year: 2023
• Print ISBN: 9788770226950
• eBook ISBN: 9781000879933
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Computer Science General
• Index: Technology & Engineering

Despite major advances in healthcare over the past century, the successful treatment of cancer has remained a significant challenge, and cancers are the second leading cause of death worldwide behind cardiovascular disease. Early detection and survival are important issues to control cancer. The development of quantitative methods and computer technology has facilitated the formation of new models in medical and biological sciences. The application of mathematical modelling in solving many real-world problems in medicine and biology has yielded fruitful results. In spite of advancements in instrumentations technology and biomedical equipment, it is not always possible to perform experiments in medicine and biology for various reasons. Thus, mathematical modelling and simulation are viewed as viable alternatives in such situations, and are discussed in this book.

The conventional diagnostic techniques of cancer are not always effective as they rely on the physical and morphological appearance of the tumour. Early stage prediction and diagnosis is very difficult with conventional techniques. It is well known that cancers are involved in genome level changes. As of now, the prognosis of various types of cancer depends upon findings related to the data generated through different experiments. Several machine learning techniques exist in analysing the data of expressed genes; however, the recent results related with deep learning algorithms are more accurate and accommodative, as they are effective in selecting and classifying informative genes. This book explores the probabilistic computational deep learning model for cancer classification and prediction.

Table of contents

• Cover Page
• Half Title page
• Series Page
• Title Page
• Copyright Page
• Contents
• Preface
• List of Figures
• List of Tables
• List of Contributors
• List of Abbreviations
• 1 Modeling of Smoking Transmission Dynamics using Caputo–Fabrizio Type Fractional Derivative
• 2 Hybrid Feature Selection Techniques Utilizing Soft Computing Methods for Cancer Data
• 3 Finite Element Technique to Explicate Calcium Diffusion in Alzheimer's Disease
• 4 Comparative Analysis of Computational Methods used in Protein-Protein Interaction (PPI) Studies
• 5 Optimization of COVID-19 Risk Factors Using Fuzzy Logic Inference System
• 6 Dynamical Analysis of the Fractional-Order Mathematical Model of Hashimoto's Thyroiditis
• 7 Heated Laminar Vertical Jet of Pseudoplastic Fluids-Against Gravity
• 8 Analytical Solutions For Hydromagnetic Flow of Chemically Reacting Williamson Fluid Over a Vertical Cone and Wedge with Heat Source/Sink
• 9 Aboodh Transform Homotopy Perturbation Method for Solving Newell–Whitehead–Segel Equation
• 10 Transmission and Control of Droplet Infection from Exotic to Native Population: A Mathematical Model
• 11 A Fractional Calculus Model to Depict the Calcium Diffusion for Neurodegenerative Disease
• 12 Approximate Analytic Solution for Tumour Growth and Human Head Heat Distribution Singular Boundary Value Model by High-Resolution Order-Preserving Fuzzy Transform
• 13 Analysis of One-Dimensional Groundwater Recharge by Spreading using Hybrid Differential Transform and Finite Difference Method
• 14 Numerical Solution of Physiological Thermoregulatory Disturbances in Cold Environment
• 15 Mathematical Modelling of Transient Heat Conduction in Biological System by Finite Element Method and Coding in MATLAB
• Index
• About the Editors