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As natural phenomena are being probed and mapped in ever-greater detail, scientists in genomics and proteomics are facing an exponentially growing vol ume of increasingly complex-structured data, information, and knowledge. Ex amples include data from microarray gene expression experiments, bead-based and microfluidic technologies, and advanced high-throughput mass spectrom etry. A fundamental challenge for life scientists is to explore, analyze, and interpret this information effectively and efficiently. To address this challenge, traditional statistical methods are being complemented by methods from data mining, machine learning and artificial intelligence, visualization techniques, and emerging technologies such as Web services and grid computing. There exists a broad consensus that sophisticated methods and tools from statistics and data mining are required to address the growing data analysis and interpretation needs in the life sciences. However, there is also a great deal of confusion about the arsenal of available techniques and how these should be used to solve concrete analysis problems. Partly this confusion is due to a lack of mutual understanding caused by the different concepts, languages, methodologies, and practices prevailing within the different disciplines.
List of contents
to Genomic and Proteomic Data Analysis.- Design Principles for Microarray Investigations.- Pre-Processing DNA Microarray Data.- Pre-Processing Mass Spectrometry Data.- Visualization in Genomics and Proteomics.- Clustering - Class Discovery in the Post-Genomic Era.- Feature Selection and Dimensionality Reduction in Genomics and Proteomics.- Resampling Strategies for Model Assessment and Selection.- Classification of Genomic and Proteomic Data Using Support Vector Machines.- Networks in Cell Biology.- Identifying Important Explanatory Variables for Time-Varying Outcomes.- Text Mining in Genomics and Proteomics.
Summary
As natural phenomena are being probed and mapped in ever-greater detail, scientists in genomics and proteomics are facing an exponentially growing vol ume of increasingly complex-structured data, information, and knowledge. Ex amples include data from microarray gene expression experiments, bead-based and microfluidic technologies, and advanced high-throughput mass spectrom etry. A fundamental challenge for life scientists is to explore, analyze, and interpret this information effectively and efficiently. To address this challenge, traditional statistical methods are being complemented by methods from data mining, machine learning and artificial intelligence, visualization techniques, and emerging technologies such as Web services and grid computing. There exists a broad consensus that sophisticated methods and tools from statistics and data mining are required to address the growing data analysis and interpretation needs in the life sciences. However, there is also a great deal of confusion about the arsenal of available techniques and how these should be used to solve concrete analysis problems. Partly this confusion is due to a lack of mutual understanding caused by the different concepts, languages, methodologies, and practices prevailing within the different disciplines.
