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This book constitutes the refereed proceedings of the International Symposium on Knowledge Exploration in Life Science Informatics, KELSI 2004, held in Milan, Italy in November 2004.
The 20 revised full papers presented were carefully reviewed and selected for inclusion in the book. Among the topics covered are proteomic data analysis, rule induction, multiple sequence alignment, pattern extraction, microarray analysis, functional data analysis, text mining, artificial life, evolutionary algorithms, randomized algorithms, feature extraction, classification, case-based learning, and bioscience education.
A Pen-and-Paper Notation for Teaching Biosciences.- An Exploration of Some Factors Affecting the Correlation of mRNA and Proteomic Data.- Improving Rule Induction Precision for Automated Annotation by Balancing Skewed Data Sets.- A Randomized Algorithm for Distance Matrix Calculations in Multiple Sequence Alignment.- Extracting Sequential Patterns for Gene Regulatory Expressions Profiles.- Data Analysis of Microarrays Using SciCraft.- Functional Data Analysis of the Dynamics of Gene Regulatory Networks.- Text Mining of Full Text Articles and Creation of a Knowledge Base for Analysis of Microarray Data.- Analysis of Protein/Protein Interactions Through Biomedical Literature: Text Mining of Abstracts vs. Text Mining of Full Text Articles.- Ranking for Medical Annotation: Investigating Performance, Local Search and Homonymy Recognition.- A New Artificial Life Formalization Model: A Worm with a Bayesian Brain.- Teaching Grasping to a Humanoid Hand as a Generalization of Human Grasping Data.- JavaSpaces – An Affordable Technology for the Simple Implementation of Reusable Parallel Evolutionary Algorithms.- Detecting and Adapting to Concept Drift in Bioinformatics.- Feature Extraction and Classification of the Auditory Brainstem Response Using Wavelet Analysis.- Evaluation of Outcome Prediction for a Clinical Diabetes Database.- Cyhrome P450 Classification of Drugs with Support Vector Machines Implementing the Nearest Point Algorithm.- Multiple-Instance Case-Based Learning for Predictive Toxicology.- Modelling and Prediction of Toxicity of Environmental Pollutants.- Modelling Aquatic Toxicity with Advanced Computational Techniques: Procedures to Standardize Data and Compare Models.