AIMC Topic: Cluster Analysis

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The neuromorphological caudate-putaminal clustering of neostriate interneurons: Kohonen self-organizing maps and supervised artificial neural networks with multivariate analysis.

Journal of theoretical biology
AIMS: The objective of this study is to investigate the possibility of the neuromorphotopological clustering of neostriate interneurons (NSIN) and their consequent classification into caudate (CIN) and putaminal neuron type (PIN), according to the nu...

ClusterTAD: an unsupervised machine learning approach to detecting topologically associated domains of chromosomes from Hi-C data.

BMC bioinformatics
BACKGROUND: With the development of chromosomal conformation capturing techniques, particularly, the Hi-C technique, the study of the spatial conformation of a genome is becoming an important topic in bioinformatics and computational biology. The Hi-...

Incorporating gene ontology into fuzzy relational clustering of microarray gene expression data.

Bio Systems
The product of gene expression works together in the cell for each living organism in order to achieve different biological processes. Many proteins are involved in different roles depending on the environment of the organism for the functioning of t...

Structured Penalized Logistic Regression for Gene Selection in Gene Expression Data Analysis.

IEEE/ACM transactions on computational biology and bioinformatics
In gene expression data analysis, the problems of cancer classification and gene selection are closely related. Successfully selecting informative genes will significantly improve the classification performance. To identify informative genes from a l...

A segmentation of brain MRI images utilizing intensity and contextual information by Markov random field.

Computer assisted surgery (Abingdon, England)
BACKGROUND AND OBJECTIVE: Image segmentation is a preliminary and fundamental step in computer aided magnetic resonance imaging (MRI) images analysis. But the performance of most current image segmentation methods is easily depreciated by noise in MR...

Stacked Autoencoders for Outlier Detection in Over-the-Horizon Radar Signals.

Computational intelligence and neuroscience
Detection of outliers in radar signals is a considerable challenge in maritime surveillance applications. High-Frequency Surface-Wave (HFSW) radars have attracted significant interest as potential tools for long-range target identification and outlie...

Consensus Kernel -Means Clustering for Incomplete Multiview Data.

Computational intelligence and neuroscience
Multiview clustering aims to improve clustering performance through optimal integration of information from multiple views. Though demonstrating promising performance in various applications, existing multiview clustering algorithms cannot effectivel...

Cervical cancer histology image identification method based on texture and lesion area features.

Computer assisted surgery (Abingdon, England)
The issue of an automated approach for detecting cervical cancer is proposed to improve the accuracy of recognition. Firstly, the cervical cancer histology source images are needed to use image preprocessing for reducing the impact brought by noise o...

Semantic biclustering for finding local, interpretable and predictive expression patterns.

BMC genomics
BACKGROUND: One of the major challenges in the analysis of gene expression data is to identify local patterns composed of genes showing coherent expression across subsets of experimental conditions. Such patterns may provide an understanding of under...

Modular representation of layered neural networks.

Neural networks : the official journal of the International Neural Network Society
Layered neural networks have greatly improved the performance of various applications including image processing, speech recognition, natural language processing, and bioinformatics. However, it is still difficult to discover or interpret knowledge f...