AIMC Topic: Cluster Analysis

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A Novel Cluster Head Selection Algorithm Based on Fuzzy Clustering and Particle Swarm Optimization.

IEEE/ACM transactions on computational biology and bioinformatics
An important objective of wireless sensor network is to prolong the network life cycle, and topology control is of great significance for extending the network life cycle. Based on previous work, for cluster head selection in hierarchical topology co...

Exploring Genome-Wide Expression Profiles Using Machine Learning Techniques.

Methods in molecular biology (Clifton, N.J.)
Although contemporary high-throughput -omics methods produce high-dimensional data, the resulting wealth of information is difficult to assess using traditional statistical procedures. Machine learning methods facilitate the detection of additional p...

DeepChrome: deep-learning for predicting gene expression from histone modifications.

Bioinformatics (Oxford, England)
MOTIVATION: Histone modifications are among the most important factors that control gene regulation. Computational methods that predict gene expression from histone modification signals are highly desirable for understanding their combinatorial effec...

Ensemble statistical and subspace clustering model for analysis of autism spectrum disorder phenotypes.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Heterogeneity in Autism Spectrum Disorder (ASD) is complex including variability in behavioral phenotype as well as clinical, physiologic, and pathologic parameters. The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-...

An unsupervised subject identification technique using EEG signals.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In this work, EEG spectral features of different subjects are uniquely mapped into a 2D feature space. Such distinctive 2D features pave the way to identify subjects from their EEG spectral characteristics in an unsupervised manner without any prior ...

[The Identification of Lettuce Varieties by Using Unsupervised Possibilistic Fuzzy Learning Vector Quantization and Near Infrared Spectroscopy].

Guang pu xue yu guang pu fen xi = Guang pu
To solve the noisy sensitivity problem of fuzzy learning vector quantization (FLVQ), unsupervised possibilistic fuzzy learning vector quantization (UPFLVQ) was proposed based on unsupervised possibilistic fuzzy clustering (UPFC). UPFLVQ aimed to use ...

Unsupervised learning technique identifies bronchiectasis phenotypes with distinct clinical characteristics.

The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease
BACKGROUND: Unsupervised learning technique allows researchers to identify different phenotypes of diseases with complex manifestations.

Unsupervised Topic Modeling in a Large Free Text Radiology Report Repository.

Journal of digital imaging
Radiology report narrative contains a large amount of information about the patient's health and the radiologist's interpretation of medical findings. Most of this critical information is entered in free text format, even when structured radiology re...

Ontology-Based Prediction and Prioritization of Gene Functional Annotations.

IEEE/ACM transactions on computational biology and bioinformatics
Genes and their protein products are essential molecular units of a living organism. The knowledge of their functions is key for the understanding of physiological and pathological biological processes, as well as in the development of new drugs and ...

Colorectal Cancer Staging Using Three Clustering Methods Based on Preoperative Clinical Findings.

Asian Pacific journal of cancer prevention : APJCP
Determination of the colorectal cancer stage is possible only after surgery based on pathology results. However, sometimes this may prove impossible. The aim of the present study was to determine colorectal cancer stage using three clustering methods...