| Description |
1 online resource |
| Series |
River Publishers series in biomedical engineering |
|
River Publishers series in biomedical engineering.
|
| Contents |
Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- List of Figures -- List of Tables -- List of Contributors -- List of Abbreviations -- Chapter 1: Modeling of Smoking Transmission Dynamics using Caputo-Fabrizio Type Fractional Derivative -- 1.1: Introduction -- 1.2: Basic Concept of Fractional Operators -- 1.3: Model Formulation -- 1.4: Caputo-Fabrizio Fractional Order Derivative -- 1.4.1: Stability Analysis of Model by Using Fixed-Point Theory -- 1.5: Numerical Results and Discussion -- 1.6: Conclusion -- References -- Chapter 2: Hybrid Feature Selection Techniques Utilizing Soft Computing Methods for Classifying Microarray Cancer Data -- 2.1: Introduction -- 2.2: Proposed Framework -- 2.2.1: Genes Extraction by ICA -- 2.2.2: Genetic Bee Colony (GBC) Algorithm -- 2.3: Used Classifier -- 2.3.1: Naive Bayes Classifier (NBC) -- 2.3.2: Support Vector Machine (SVM) Classifier -- 2.4: Experimental Setups -- 2.5: Experimental Result -- 2.6: Conclusion -- References -- Chapter 3: Finite Element Technique to Explicate Calcium Diffusion in Alzheimer's Disease -- 3.1: Introduction -- 3.2: Literature Survey -- 3.3: Mathematical Formulations -- 3.3.1: Calcium Buffering -- 3.3.2: Voltage Gated Calcium Channel (VGCC) -- 3.3.3: Endoplasmic Reticulum (ER) -- 3.4: The Finite Element Technique -- 3.4.1: Approximated Geometry of the Cell -- 3.4.2: Physiological Boundary Conditions -- 3.4.3: Meshing of the domain -- 3.5: Results and Discussion -- 3.5.1: For Hippocampal Neuron -- 3.5.2: For Basal Forebrain Neuron -- 3.6: Conclusion -- References -- Chapter 4: Comparative Analysis of Computational Methods used in Protein-Protein Interaction (PPI) Studies -- 4.1: Introduction -- 4.1.1: Protein -- 4.1.2: Protein-Protein Interaction -- 4.1.2.1: Protein-protein interfacial characteristics -- 4.1.2.1.1: Size and shape |
|
4.1.2.1.2: Complementarity between surfaces -- 4.1.2.1.3: Residue interface propensities -- 4.1.2.1.4: Hydrophobicity including Hydrogen bonding -- 4.1.2.1.5: Segmentation and secondary structure -- 4.1.2.1.6: Conformational changes on complex formation -- 4.1.2.2: PPI types -- 4.1.2.2.1: Homo oligomeric and Hetero oligomeric -- 4.1.2.2.2: Obligate and non obligate complexes -- 4.1.2.2.3: Transient and permanent complexes -- 4.1.2.2.4: Disordered to ordered complexes -- 4.1.2.3: PPI methods classification -- 4.2: In Silico Methods -- 4.2.1: Sequence Based Approaches -- 4.2.1.1: Ortholog based sequence approach -- 4.2.1.2: Domain pairs-based sequence approach -- 4.2.1.3: Statistical sequence-based approaches -- 4.2.1.3.1: Mirror tree method -- 4.2.1.3.2: PIPE -- 4.2.1.3.3: Co-evolutionary divergence -- 4.2.1.4: Machine learning sequence-based approaches -- 4.2.1.4.1: Auto-covariance -- 4.2.1.4.2: Pairwise similarity -- 4.2.1.4.3: Amino acid composition -- 4.2.1.4.4: Amino acid triad -- 4.2.1.4.5: UNISPPI -- 4.2.1.4.6: ETB viterbi -- 4.2.2: Structure Based Approaches -- 4.2.2.1: Template structure-based approaches -- 4.2.2.1.1: PRISM -- 4.2.2.1.2: PREPPI -- 4.2.2.2: Statistical structure-based approach -- 4.2.2.2.1: PID matrix score -- 4.2.2.2.2: Pre SPI -- 4.2.2.2.3: Domain cohesion and coupling -- 4.2.2.2.4: MEGADOCK -- 4.2.2.2.5: MetaApproach -- 4.2.2.3: Machine learning structure-based approaches -- 4.2.2.3.1: Random forest -- 4.2.2.3.2: Struct2Net -- 4.2.3: Gene Neighbourhood -- 4.2.4: Gene Fusion -- 4.2.5: In Silico Two-hybrid (I2h) -- 4.2.6: Phylogenetic Tree -- 4.2.7: Phylogenetic Profile -- 4.2.8: Gene Expression -- 4.3: PPI Networks and Databases -- 4.3.1: Creation of the PPI Networks -- 4.3.1.1: Choice of databases and data selection -- 4.3.1.2: Visualising PPI network -- 4.3.2: Different Databases -- 4.3.2.1: Interaction database |
|
4.3.2.2: Metamining databases -- 4.3.2.3: Predictive interaction databases -- 4.3.2.4: Pathway database -- 4.3.2.5: Unifying database -- 4.4: Softwares Available for PPI -- 4.5: Conclusion -- References -- Chapter 5: Optimization of COVID-19 Risk Factors Using Fuzzy Logic Inference System -- 5.1: Introduction -- 5.2: Methodology -- 5.2.1: Proposed Mamdani Fuzzy Control System -- 5.2.2: Fuzzy Controller Design -- 5.2.3: Parameters Identification -- 5.2.4: Fuzzification -- 5.2.5: Fuzzy Inference Rule Base -- 5.2.6: Rule Evaluation By Fuzzy Inference Engine -- 5.2.7: Defuzzification -- 5.3: Results -- 5.4: Discussion -- 5.5: Conclusion and Future Work -- References -- Chapter 6: Dynamical Analysis of the Fractional-Order Mathematical Model of Hashimoto's Thyroiditis -- 6.1: Introduction -- 6.2: Preliminaries -- 6.3: Formulation of Fractional-Order Model of Hashimoto's Thyroiditis -- 6.4: Stability Analysis -- 6.5: Construction of a Numerical Solution Scheme -- 6.6: Numerical Segment -- 6.7: Conclusions -- References -- Chapter 7: Heated Laminar Vertical Jet of Pseudoplastic Fluids-Against Gravity -- 7.1: Introduction -- 7.2: Basic Equations -- 7.3: Results and Discussions -- 7.4: Graphical Presentation -- 7.5: Conclusion -- References -- Chapter 8: Analytical Solutions For Hydromagnetic Flow of Chemically Reacting Williamson Fluid Over a Vertical Cone and Wedge with Heat Source/Sink -- 8.1: Introduction -- 8.2: Formulation of the Problem -- 8.3: Solution of the Problem -- 8.4: Results and Discussion -- 8.5: Conclusion -- References -- Chapter 9: Aboodh Transform Homotopy Perturbation Method for Solving Newell-Whitehead-Segel Equation -- 9.1: Introduction -- 9.2: Basic Definition of Aboodh Transform -- 9.2.1: Some Properies of Aboodh Transform -- 9.3: Idea of Aboodh Transform Homotopy Perturbation Method -- 9.4: Some Illustrations -- 9.5: Conclusion |
|
Chapter 13: Analysis of One-Dimensional Groundwater Recharge by Spreading using Hybrid Differential Transform and Finite Difference Method -- 13.1: Introduction -- 13.2: Research Gap -- 13.3: Mathematical Formulation -- 13.4: Methodology -- 13.5: Hybrid Differential Transform and Finite Difference Method -- 13.6: Solution -- 13.7: Results and Discussion -- 13.8: Conclusions -- 13.9: Utilities of Research -- References -- Chapter 14: Numerical Solution of Physiological Thermoregulatory Disturbances in Cold Environment -- 14.1: Introduction -- 14.2: Material and Methods -- 14.3: Result -- 14.4: Discussion -- References -- Chapter 15: Mathematical Modelling of Transient Heat Conduction in Biological System by Finite Element Method and Coding in MATLAB -- 15.1: Introduction -- 15.2: General Procedure of Finite Element Method -- 15.3: Process of Finite Element Method -- 15.3.1: Definition of the Problem and its Domain: One Dimensional Thermal Equation of Biological System -- 15.4: Steps Involved in Finite Element Process -- 15.5: Model-2: One Dimensional Quadratic Interpolation Model -- 15.5.1: Assumption of a Suitable form of Variation in T for Quadratic Element -- 15.6: Assembly of Elements -- 15.7: Matrix Form of Element Equation -- 15.8: Algorithm and Computer Program to Solve Heat Equation using FEM in Matlab: -- 15.9: Result and Discussion -- References -- Index -- About the Editors |
| Summary |
"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"-- Provided by publisher |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from PDF title page (Taylor & Francis, viewed November 29, 2023) |
| Subject |
Bioinformatics -- Mathematical models
|
|
Machine learning.
|
|
Cancer -- Research -- Methodology
|
|
Machine Learning
|
|
Bioinformática -- Modelos matemáticos
|
|
Aprendizaje automático
|
|
Cáncer -- Investigación -- Metodología
|
|
Bioinformatics
|
|
Cancer -- Diagnosis -- Data processing
|
|
Cancer -- Diagnosis -- Mathematical models
|
|
Machine learning
|
| Form |
Electronic book
|
| Author |
Makrariya, Akshara editor
|
|
Jha, Brajesh Kumar, editor.
|
|
Musheer, Rabia editor
|
|
Shukla, Anant Kant editor
|
|
Jha, Amrita editor
|
|
Naik, Parvaiz Ahmad editor
|
| ISBN |
9781000879872 |
|
1000879879 |
|
9781003393238 |
|
1003393233 |
|
9781000879933 |
|
1000879933 |
|
8770226946 |
|
9788770226943 |
|
