Computational Biology: Issues and Applications in Oncology

ISBN-13: 9781441908100 / Angielski / Twarda / 2009 / 310 str.

Tuan Pham

Opis

Computational biology is an interdisciplinary research that applies approaches and methodologies of information sciences and engineering to address complex pr- lems in biology. With rapid developments in the omics and computer technologies over the past decade, computational biology has been evolving to cover a much wider research domain and applications in order to adequately address challenging problems in systems biology and medicine. This edited book focuses on recent - sues and applications of computational biology in oncology. This book contains 11 chapters that cover diverse advanced computationalmethods applied to oncologyin an attempt to ?nd more effective ways for the diagnosis and cure of cancer. Chapter 1 by Chen and Nguyen addresses an analysis of cancer genomics data using partial least squares weights for identifying relevant genes, which are useful for follow-up validations. In Chap. 2, Zhao and Yan report an interesting biclust- ing method for microarray data analysis, which can handle the case when only a subset of genes coregulates under a subset of conditions and appears to be a novel technique for classifying cancer tissues. As another computational method for - croarray data analysis, the work by Le Cao and McLachlan in Chap. 3 discusses the dif?culties encountered when dealing with microarray data subjected to sel- tion bias, multiclass, and unbalanced problems, which can be overcome by careful selection of gene expression pro?les. Novel methods presented in these chapters can be applied for developing diagnostic tests and therapeutic treatments for cancer patients.

Spis treści

Computational Biology: Issues and Applications in Oncology Editor: Tuan D. Pham List of Chapters Chapter 1 Identification of relevant genes from microarray experiments based on partial least squares weights: Application to cancer genomics Ying Chen, Danh V. Nguyen Chapter 2 Geometric biclustering and its applications to cancer tissue classification based on DNA microarray gene expression data Hongya Zhao, Hong Yan Chapter 3 Statistical analysis on microarray data: selection of gene prognosis signatures Kim-Anh Lê Cao, Geoffrey J. McLachlan Chapter 4 Agent-based modeling of ductal carcinoma in situ: application to patient-specific breast cancer modeling Paul Macklin, Jahun Kim, Giovanna Tomaiuolo, Mary E. Edgerton, Vittorio Cristini Chapter 5 Multi-cluster class based classification for the diagnosis of suspicious areas in digital mammograms Brijesh Verma Chapter 6 Analysis of cancer data using evolutionary computation Cuong C. To, Tuan D. Pham Chapter 7 Analysis of population-based genetic association studies applied to cancer susceptibility and prognosis Xavier Solé, Juan Ramón González, Víctor Moreno Chapter 8 Selected applications of graph-based tracking methods for cancer research Pascal Vallotton, Lilian Soon Chapter 9 Recent advances in cell classification for cancer research and drug discovery Dat T. Tran, Tuan D. Pham Chapter 10 Computational tools and resources for systems biology approaches in cancer Andriani Daskalaki, Christoph Wierling, Ralf Herwig Chapter 11 Laser speckle imaging for blood flow analyses Thinh M. Le, J.S. Paul,H. Al-Nashash, A. Tan, A.R. Luft, F-S. Sheu, S.H. Ong Chapter 12 The Challenges in Blood Proteomic Biomarker Discovery Guangxu Jin, Xiaobo Zhou, Honghui Wang, Stephen T.C. Wong

Blurb

Computational Biology: Issues and Applications in Oncology provides a comprehensive report on recent techniques and results in computational oncology essential to the knowledge of scientists, engineers, as well as postgraduate students working on the areas of computational biology, bioinformatics, and medical informatics.

With chapters timely prepared and written by experts in the field, this in-depth and up-to-date volume covers advanced statistical methods, heuristic algorithms, cluster analysis, data modeling, image and pattern analysis applied to cancer research. The literature and coverage of a spectrum of key topics in issues and applications in oncology make this a useful resource to computational life-science researchers wishing to enhance the most recent knowledge to facilitate their own investigations.