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The First International Workshop on Applications of Machine Learning Techniques in Medicine and Biology
MLMB 2010
February 10-15, 2010 - St. Maarten, Netherlands Antilles
link: http://www.iaria.org/conferences2010/MLMB.html#CallForPapers
Machine learning (ML) is an inherently interdisciplinary field, built on concepts from artificial intelligence, cognitive science, probability and statistics, information theory, philosophy, control theory, psychology, neurobiology and other fields. ML techniques have found widespread applications in biology and medicine. Medicine is largely an evidence-driven discipline where large quantities of relatively high-quality data are collected and stored in databases. The medical data are highly heterogeneous and are stored in numerical, text, image, sound and video formats. They include clinical data (symptoms, demographics, biochemical tests, diagnoses and various imaging, video, vital signals, etc), logistics data (charges and costs, policies, guidelines, clinical trials, etc), bibliographical data, and molecular data. Bioinformatics, which concerns the latter type of data, conceptualizes biology in terms of molecules and applies "informatics" techniques, derived from disciplines such as applied mathematics, computer science and statistics to understand and organize the information associated with these molecules on a large scale. In other words, bioinformatics encompasses analysis of molecular data expressed in the form of nucleotides, amino acids, DNA, RNA, pedtides and proteins. The sheer amount and breadth of data requires development of efficient methods for knowledge/information extraction that can cope with the size and complexity of the accumulated data. There are numerous examples of successful applications of machine learning in areas of diagnosis and prevention, prognosis and therapeutic decision making.
This workshop aims to bridge the gap between machine learning and biomedical informatics, and provide a platform to present and discuss recent advancements in the area of application of ML techniques in biology and medicine.
We encourage papers on novel machine learning algorithms including, but not limited to:
* Decision tree learning
* Artificial neural network learning
* Bayesian learning
* Computational learning theory
* Instance-Based learning
* Genetic algorithms
* Learning sets of rules
* Analytical learning
* Probabilistic learning
* Inductive logic programming
* Evolution learning
* Reinforcement learning
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