Analysis of Tiling Microarray Data by Learning Vector Quantization and Relevance Learning
نویسندگان
چکیده
We apply learning vector quantization to the analysis of tiling microarray data. As an example we consider the classification of C. elegans genomic probes as intronic or exonic. Training is based on the current annotation of the genome. Relevance learning techniques are used to weight and select features according to their importance for the classification. Among other findings, the analysis suggests that correlations between the perfect match intensity of a particular probe and its neighbors are highly relevant for successful exon identification.
منابع مشابه
Regularization in Relevance Learning Vector Quantization Using l one Norms
We propose in this contribution a method for l1-regularization in prototype based relevance learning vector quantization (LVQ) for sparse relevance profiles. Sparse relevance profiles in hyperspectral data analysis fade down those spectral bands which are not necessary for classification. In particular, we consider the sparsity in the relevance profile enforced by LASSO optimization. The latter...
متن کاملRegularization in relevance learning vector quantization using l1-norms
We propose in this contribution a method for l1-regularization in prototype based relevance learning vector quantization (LVQ) for sparse relevance pro les. Sparse relevance pro les in hyperspectral data analysis fade down those spectral bands which are not necessary for classi cation. In particular, we consider the sparsity in the relevance pro le enforced by LASSO optimization. The latter one...
متن کاملNGTSOM: A Novel Data Clustering Algorithm Based on Game Theoretic and Self- Organizing Map
Identifying clusters is an important aspect of data analysis. This paper proposes a noveldata clustering algorithm to increase the clustering accuracy. A novel game theoretic self-organizingmap (NGTSOM ) and neural gas (NG) are used in combination with Competitive Hebbian Learning(CHL) to improve the quality of the map and provide a better vector quantization (VQ) for clusteringdata. Different ...
متن کاملBiomedical Applications of Prototype Based Classifiers and Relevance Learning
In this contribution, prototype-based systems and relevance learning are presented and discussed in the context of biomedical data analysis. Learning Vector Quantization and Matrix Relevance Learning serve as the main examples. After introducing basic concepts and related approaches, example applications of Generalized Matrix Relevance Learning are reviewed, including the classification of adre...
متن کاملNeural Techniques for Improving the Classification Accuracy of Microarray Data Set using Rough Set Feature Selection Method
-Classification, a data mining task is an effective method to classify the data in the process of Knowledge Data Discovery. Classification method algorithms are widely used in medical field to classify the medical data for diagnosis. Feature Selection increases the accuracy of the Classifier because it eliminates irrelevant attributes. This paper analyzes the performance of neural network class...
متن کامل