Computational and Analytic Methods in Biological Sciences
GBP 99.99
Kattintson ide a feliratkozáshoz
A Prosperónál jelenleg nincsen raktáron.
| ISBN13: | 9788770226950 |
| ISBN10: | 8770226954 |
| Kötéstípus: | Keménykötés |
| Terjedelem: | 324 oldal |
| Méret: | 234x156 mm |
| Súly: | 660 g |
| Nyelv: | angol |
| Illusztrációk: | 10 Illustrations, black & white; 77 Illustrations, color; 34 Tables, black & white |
| 726 |
Mathematical modelling and simulation are viewed as viable alternatives to performing experiments in medicine and biology, and are discussed in this book. The book also explores the probabilistic computational deep learning model for cancer classification and prediction.
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.
Preface Participants of the Reviewing Process 1. Optimal Homotopy Analysis of a Nonlinear Fractional
-order Model for HTLV
-1 Infection of CD4+ T
-cells 2. An Optional Additive Randomized Response Model Smearing Optimization Technique 3. Challenges of Microarray Data Analysis 4. Modeling and Analysis of an SEIVR Model for the Transmission Dynamics of HBV Epidemics with Optimal Control 5. Support Vector Machine Classification of Biomedical Data with a Novel Wrapper Based Machine Learning Approach 6. New Nature Inspired Framework Using Hybrid Gene Selection Techniques for Microarray Data Classification 7. Deep Learning Classification and Prediction with Metaheuristic Algorithm for High Dimensional Biomedical Datasets 8. Soft Computing Method for Machine Learning Classification of Microarray Gene Expression Data 9. Fractional Modelling of Calcium Distribution in Hepatocytes 10. Numerical Modelling and Simulation of Calcium Distribution in Astrocytes 11. Analytical Estimated Solution of the Modelling of Tumor Polyclonality 12. Impact of Thermal Radiation and Nanoparticle Shape on Au/Al2O3
-blood Nanofluid in a Permeable pipe using HAM 13. Series Solution for Thermal
-diffusion, Diffusion
-thermo Effects on MHD Flow in a Porous Channel with Moving/stationary Walls using HAM 14. The Numerical Solution of Prolate and Oblate Ellipsoidal Shaped Bio Heat Equations using the Finite Element Method 15. Numerical Analysis of Heat Flow in a Human Body in a Cold Environment 16. Computational Study of Breast Fibrosis
