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"This volume is a collection of state-of-the-art research into the application of data mining to the discovery and analysis of new biomarkers. Presenting new results, models and algorithms, the included contributions focus on biomarker data integration, information retrieval methods, and statistical machine learning techniques."

"Extracting and applying knowledge from chemical, biological, and clinical data is one of the biggest problems for the pharmaceutical industry. Focusing on diverse data mining approaches for drug discovery, including chemogenomics, toxicogenomics, and individual drug response prediction, Pharmaceutical Data Mining links theory to applications to illustrate how sophisticated computational data mining techniques can impact contemporary drug discovery and development. The book is of vital interest to pharmaceutical scientists, principal investigators and scientists, research directors, industrial."

"The two-volume set LNAI 7301 and 7302 constitutes the refereed proceedings of the 16th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2012, held in Kuala Lumpur, Malaysia, in May 2012. The total of 20 revised full papers and 66 revised short papers were carefully reviewed and selected from 241 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD-related areas. The papers are organized in topical sections on supervised learning, active, ensemble, rare-class and online, unsupervised learning, clustering, probabilistic modeling in the first volume and on pattern mining, networks, graphs, time-series and outlier detection, and data manipulation, pre-processing and dimension reduction in the second volume. "

"The two-volume set LNAI 7301 and 7302 constitutes the refereed proceedings of the 16th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2012, held in Kuala Lumpur, Malaysia, in May 2012. The total of 20 revised full papers and 66 revised short papers were carefully reviewed and selected from 241 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD-related areas. The papers are organized in topical sections on supervised learning: active, ensemble, rare-class and online; unsupervised learning: clustering, probabilistic modeling in the first volume and on pattern mining: networks, graphs, time-series and outlier detection, and data manipulation: pre-processing and dimension reduction in the second volume."

"Summary:Equips you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets. This title focuses on important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data."