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Biometry --- Biology --- Biometry. --- Biology.

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This book is organized into 4 sections, each looking at the question of outcome prediction in cancer from a different angle. The first section describes the clinical problem and some of the predicaments that clinicians face in dealing with cancer. Amongst issues discussed in this section are the TNM staging, accepted methods for survival analysis and competing risks. The second section describes the biological and genetic markers and the r̥le of bioinformatics. Understanding of the genetic and environmental basis of cancers will help in identifying high-risk populations and developing effective prevention and early detection strategies. The third section provides technical details of mathematical analysis behind survival prediction backed up by examples from various types of cancers. The fourth section describes a number of machine learning methods which have been applied to decision support in cancer. The final section describes how information is shared within the scientific and medical communities and with the general population using information technology and the World Wide Web. * Applications cover 8 types of cancer including brain, eye, mouth, head and neck, breast, lungs, colon and prostate * Include contributions from authors in 5 different disciplines * Provides a valuable educational tool for medical informatics.

Biomathematics. Biometry. Biostatistics

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Biomathematics. Biometry. Biostatistics --- Mathematical statistics --- Biometry. --- Biométrie --- Biométrie

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Statistics as Topic --- Probability --- Biometry

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To adequately describe complex spatio-temporal processes that occur in multi-cellular organisms, a class of models is required that simultaneously takes into account differences between individual cells as well as their ability to communicate and interact with one another and their environment. Single-cell-based models form a framework that allows for the explicit incorporation of different properties of individual cells, but at the same time enables all cells to act together as one collective body. This leads ultimately to more biologically realistic models of heterogeneous tissues and multi-cellular organisms and allows for a better understanding of the principles underlying the complex biological processes occurring during the formation, growth and maintenance of multi-cellular bodies. The aim of this book is to assemble a collection of different mathematical and computational models and techniques that focus on individual cells, cell processes and cell behaviour, that are also suitable to address problems on the multi-cellular or tissue scale. We would like to focus the level of the book equally to students starting their research in the field of mathematical biology and to scientists already modelling multi-cellular processes. Therefore, our intention is to include in this book a detailed description of each model and an extensive review of suitable biological and medical applications.

Biomathematics. Biometry. Biostatistics --- Biology --- biomathematica --- biologie --- biometrie