AI Medical Compendium Topic:
Cluster Analysis

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Semi-Supervised Fuzzy Clustering with Feature Discrimination.

PloS one
Semi-supervised clustering algorithms are increasingly employed for discovering hidden structure in data with partially labelled patterns. In order to make the clustering approach useful and acceptable to users, the information provided must be simpl...

Investigation on Amari's dynamical neural field with global constant inhibition.

Neural networks : the official journal of the International Neural Network Society
In this paper, the properties of Amari's dynamical neural field with global constant inhibition induced by its kernel are investigated. Amari's dynamical neural field illustrates many neurophysiological phenomena successfully and has been applied to ...

Incremental multi-class semi-supervised clustering regularized by Kalman filtering.

Neural networks : the official journal of the International Neural Network Society
This paper introduces an on-line semi-supervised learning algorithm formulated as a regularized kernel spectral clustering (KSC) approach. We consider the case where new data arrive sequentially but only a small fraction of it is labeled. The availab...

Machine Learnable Fold Space Representation based on Residue Cluster Classes.

Computational biology and chemistry
MOTIVATION: Protein fold space is a conceptual framework where all possible protein folds exist and ideas about protein structure, function and evolution may be analyzed. Classification of protein folds in this space is commonly achieved by using sim...

Use of Semisupervised Clustering and Feature-Selection Techniques for Identification of Co-expressed Genes.

IEEE journal of biomedical and health informatics
Studying the patterns hidden in gene-expression data helps to understand the functionality of genes. In general, clustering techniques are widely used for the identification of natural partitionings from the gene expression data. In order to put cons...

Optimal combination of feature selection and classification via local hyperplane based learning strategy.

BMC bioinformatics
BACKGROUND: Classifying cancers by gene selection is among the most important and challenging procedures in biomedicine. A major challenge is to design an effective method that eliminates irrelevant, redundant, or noisy genes from the classification,...

Machine learning approach for pooled DNA sample calibration.

BMC bioinformatics
BACKGROUND: Despite ongoing reduction in genotyping costs, genomic studies involving large numbers of species with low economic value (such as Black Tiger prawns) remain cost prohibitive. In this scenario DNA pooling is an attractive option to reduce...

Unsupervised lineage-based characterization of primate precursors reveals high proliferative and morphological diversity in the OSVZ.

The Journal of comparative neurology
Generation of the primate cortex is characterized by the diversity of cortical precursors and the complexity of their lineage relationships. Recent studies have reported miscellaneous precursor types based on observer classification of cell biology f...

A k-mer-based barcode DNA classification methodology based on spectral representation and a neural gas network.

Artificial intelligence in medicine
OBJECTIVES: In this paper, an alignment-free method for DNA barcode classification that is based on both a spectral representation and a neural gas network for unsupervised clustering is proposed.