Ulteriori informazioni
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.
Sommario
Identification of Relevant Genes from Microarray Experiments based on Partial Least Squares Weights: Application to Cancer Genomics.- Geometric Biclustering and Its Applications to Cancer Tissue Classification Based on DNA Microarray Gene Expression Data.- Statistical Analysis on Microarray Data: Selection of Gene Prognosis Signatures.- Agent-Based Modeling of Ductal Carcinoma In Situ: Application to Patient-Specific Breast Cancer Modeling.- Multicluster Class-Based Classification for the Diagnosis of Suspicious Areas in Digital Mammograms.- Analysis of Cancer Data Using Evolutionary Computation.- Analysis of Population-Based Genetic Association Studies Applied to Cancer Susceptibility and Prognosis.- Selected Applications of Graph-Based Tracking Methods for Cancer Research.- Recent Advances in Cell Classification for Cancer Research and Drug Discovery.- Computational Tools and Resources for Systems Biology Approaches in Cancer.- Laser Speckle Imaging for Blood Flow Analysis.- The Challenges in Blood Proteomic Biomarker Discovery.
Riassunto
This volume covers techniques in computational biology and their applications in oncology. It details advanced statistical methods, heuristic algorithms, cluster analysis, data modeling, and image and pattern analysis applied to cancer research.
