Quantitative Medical Data Analysis Using Mathematical Tools and Statistical Techniques

Quantitative biomedical data analysis is a fast-growing interdisciplinary area of applied and computational mathematics, statistics, computer science, and biomedical science, leading to new fields such as bioinformatics, biomathematics, and biostatistics. In addition to traditional statistical techniques and mathematical models using differential equations, new developments with a very broad spectrum of applications, such as wavelets, spline functions, curve and surface subdivisions, sampling, and learning theory, have found their mathematical home in biomedical data analysis.This book gives a new and integrated introduction to quantitative medical data analysis from the viewpoint of biomathematicians, biostatisticians, and bioinformaticians. It offers a definitive resource to bridge the disciplines of mathematics, statistics, and biomedical sciences. Topics include mathematical models for cancer invasion and clinical sciences, data mining techniques and subset selection in data analysis, survival data analysis and survival models for cancer patients, statistical analysis and neural network techniques for genomic and proteomic data analysis, wavelet and spline applications for mass spectrometry data preprocessing and statistical computing.

... many of the chapters could be of interest for mathematicians searching for inspiration or motivation for an axiomatic general working formulation. The topics chosen are nontrivial and provide working examples where one can learn directly from experience. So, this book could be very helpful for PhD students and researchers who are beginning in the field, but experts could also find it useful (and be amused) to read concrete explicit realizations of many topics usually treated only at an abstract level. --Mathematical Reviews