AI Medical Compendium Topic:
Cluster Analysis

Clear Filters Showing 991 to 1000 of 1337 articles

Differentiating Sense through Semantic Interaction Data.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Words which have different representations but are semantically related, such as dementia and delirium, can pose difficult issues in understanding text. We explore the use of interaction frequency data between semantic elements as a means to differen...

A machine learning approach for ranking clusters of docked protein-protein complexes by pairwise cluster comparison.

Proteins
Reliable identification of near-native poses of docked protein-protein complexes is still an unsolved problem. The intrinsic heterogeneity of protein-protein interactions is challenging for traditional biophysical or knowledge based potentials and th...

Orthogonal self-guided similarity preserving projection for classification and clustering.

Neural networks : the official journal of the International Neural Network Society
A suitable feature representation can faithfully preserve the intrinsic structure of data. However, traditional dimensionality reduction (DR) methods commonly use the original input features to define the intrinsic structure, which makes the estimate...

Self-Taught convolutional neural networks for short text clustering.

Neural networks : the official journal of the International Neural Network Society
Short text clustering is a challenging problem due to its sparseness of text representation. Here we propose a flexible Self-Taught Convolutional neural network framework for Short Text Clustering (dubbed STC), which can flexibly and successfully inc...

Machine-learned cluster identification in high-dimensional data.

Journal of biomedical informatics
BACKGROUND: High-dimensional biomedical data are frequently clustered to identify subgroup structures pointing at distinct disease subtypes. It is crucial that the used cluster algorithm works correctly. However, by imposing a predefined shape on the...

Ensemble Clustering Classification compete SVM and One-Class classifiers applied on plant microRNAs Data.

Journal of integrative bioinformatics
The performance of many learning and data mining algorithms depends critically on suitable metrics to assess efficiency over the input space. Learning a suitable metric from examples may, therefore, be the key to successful application of these algor...

Automated identification of Monogeneans using digital image processing and K-nearest neighbour approaches.

BMC bioinformatics
BACKGROUND: Monogeneans are flatworms (Platyhelminthes) that are primarily found on gills and skin of fishes. Monogenean parasites have attachment appendages at their haptoral regions that help them to move about the body surface and feed on skin and...

Grouping miRNAs of similar functions via weighted information content of gene ontology.

BMC bioinformatics
BACKGROUND: Regulation mechanisms between miRNAs and genes are complicated. To accomplish a biological function, a miRNA may regulate multiple target genes, and similarly a target gene may be regulated by multiple miRNAs. Wet-lab knowledge of co-regu...

Gogadget: An R Package for Interpretation and Visualization of GO Enrichment Results.

Molecular informatics
Gene expression profiling followed by gene ontology (GO) term enrichment analysis can generate long lists of significant GO terms. To interpret these results and get biological insight in the data, filtering and rearranging these long lists of GO ter...