Similarity-Based Clustering: Recent Developments and Biomedical Applications
Similarity-based learning methods have a great potential as an intuitive and flexible toolbox for mining, visualization,and inspection of largedata sets. They combine simple and human-understandable principles, such as distance-based classification, prototypes, or Hebbian learning, with a large variety of different, problem-adapted design choices, such as a data-optimum topology, similarity measure, or learning mode. In medicine, biology, and medical bioinformatics, more and more data arise from clinical measurements such as EEG or fMRI studies for monitoring brain activity, mass spectrometry data for the detection of proteins, peptides and composites, or microarray profiles for the analysis of gene expressions. Typically, data are high-dimensional, noisy, and very hard to inspect using classic (e. g. , symbolic or linear) methods. At the same time, new technologies ranging from the possibility of a very high resolution of spectra to high-throughput screening for microarray data are rapidly developing and carry the promise of an efficient, cheap, and automatic gathering of tons of high-quality data with large information potential. Thus, there is a need for appropriate - chine learning methods which help to automatically extract and interpret the relevant parts of this information and which, eventually, help to enable und- standing of biological systems, reliable diagnosis of faults, and therapy of diseases such as cancer based on this information. Moreover, these application scenarios pose fundamental and qualitatively new challenges to the learning systems - cause of the specifics of the data and learning tasks. Since these characteristics are particularly pronounced within the medical domain, but not limited to it and of principled interest, this research topic opens the way towardimportant new directions of algorithmic design and accompanying theory.
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Similarity-Based Clustering: Recent Developments and Biomedical Applications
Similarity-based learning methods have a great potential as an intuitive and flexible toolbox for mining, visualization,and inspection of largedata sets. They combine simple and human-understandable principles, such as distance-based classification, prototypes, or Hebbian learning, with a large variety of different, problem-adapted design choices, such as a data-optimum topology, similarity measure, or learning mode. In medicine, biology, and medical bioinformatics, more and more data arise from clinical measurements such as EEG or fMRI studies for monitoring brain activity, mass spectrometry data for the detection of proteins, peptides and composites, or microarray profiles for the analysis of gene expressions. Typically, data are high-dimensional, noisy, and very hard to inspect using classic (e. g. , symbolic or linear) methods. At the same time, new technologies ranging from the possibility of a very high resolution of spectra to high-throughput screening for microarray data are rapidly developing and carry the promise of an efficient, cheap, and automatic gathering of tons of high-quality data with large information potential. Thus, there is a need for appropriate - chine learning methods which help to automatically extract and interpret the relevant parts of this information and which, eventually, help to enable und- standing of biological systems, reliable diagnosis of faults, and therapy of diseases such as cancer based on this information. Moreover, these application scenarios pose fundamental and qualitatively new challenges to the learning systems - cause of the specifics of the data and learning tasks. Since these characteristics are particularly pronounced within the medical domain, but not limited to it and of principled interest, this research topic opens the way towardimportant new directions of algorithmic design and accompanying theory.
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Similarity-Based Clustering: Recent Developments and Biomedical Applications

Similarity-Based Clustering: Recent Developments and Biomedical Applications

Similarity-Based Clustering: Recent Developments and Biomedical Applications

Similarity-Based Clustering: Recent Developments and Biomedical Applications

Overview

Similarity-based learning methods have a great potential as an intuitive and flexible toolbox for mining, visualization,and inspection of largedata sets. They combine simple and human-understandable principles, such as distance-based classification, prototypes, or Hebbian learning, with a large variety of different, problem-adapted design choices, such as a data-optimum topology, similarity measure, or learning mode. In medicine, biology, and medical bioinformatics, more and more data arise from clinical measurements such as EEG or fMRI studies for monitoring brain activity, mass spectrometry data for the detection of proteins, peptides and composites, or microarray profiles for the analysis of gene expressions. Typically, data are high-dimensional, noisy, and very hard to inspect using classic (e. g. , symbolic or linear) methods. At the same time, new technologies ranging from the possibility of a very high resolution of spectra to high-throughput screening for microarray data are rapidly developing and carry the promise of an efficient, cheap, and automatic gathering of tons of high-quality data with large information potential. Thus, there is a need for appropriate - chine learning methods which help to automatically extract and interpret the relevant parts of this information and which, eventually, help to enable und- standing of biological systems, reliable diagnosis of faults, and therapy of diseases such as cancer based on this information. Moreover, these application scenarios pose fundamental and qualitatively new challenges to the learning systems - cause of the specifics of the data and learning tasks. Since these characteristics are particularly pronounced within the medical domain, but not limited to it and of principled interest, this research topic opens the way towardimportant new directions of algorithmic design and accompanying theory.

Product Details

ISBN-13: 9783642018046
Publisher: Springer Berlin Heidelberg
Publication date: 07/08/2009
Series: Lecture Notes in Computer Science , #5400
Edition description: 2009
Pages: 203
Product dimensions: 5.90(w) x 9.10(h) x 0.20(d)

Table of Contents

I: Dynamics of Similarity-Based Clustering.- Statistical Mechanics of On-line Learning.- Some Theoretical Aspects of the Neural Gas Vector Quantizer.- Immediate Reward Reinforcement Learning for Clustering and Topology Preserving Mappings.- II: Information Representation.- Advances in Feature Selection with Mutual Information.- Unleashing Pearson Correlation for Faithful Analysis of Biomedical Data.- Median Topographic Maps for Biomedical Data Sets.- Visualization of Structured Data via Generative Probabilistic Modeling.- III: Particular Challenges in Applications.- Learning Highly Structured Manifolds: Harnessing the Power of SOMs.- Estimation of Boar Sperm Status Using Intracellular Density Distribution in Grey Level Images.- HIV-1 Drug Resistance Prediction and Therapy Optimization: A Case Study for the Application of Classification and Clustering Methods.
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