AIMC Topic: Cluster Analysis

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A Systematic Evaluation of Interneuron Morphology Representations for Cell Type Discrimination.

Neuroinformatics
Quantitative analysis of neuronal morphologies usually begins with choosing a particular feature representation in order to make individual morphologies amenable to standard statistics tools and machine learning algorithms. Many different feature rep...

[Human activity recognition based on the inertial information and convolutional neural network].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
With the rapid improvement of the perception and computing capacity of mobile devices such as smart phones, human activity recognition using mobile devices as the carrier has been a new research hot-spot. The inertial information collected by the acc...

Machine learning and statistical methods for clustering single-cell RNA-sequencing data.

Briefings in bioinformatics
UNLABELLED: Single-cell RNAsequencing (scRNA-seq) technologies have enabled the large-scale whole-transcriptome profiling of each individual single cell in a cell population. A core analysis of the scRNA-seq transcriptome profiles is to cluster the s...

Unsupervised stratification in neuroimaging through deep latent embeddings.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
There is growing evidence that the use of stringent and dichotomic diagnostic categories in many medical disciplines (particularly 'brain sciences' as neurology and psychiatry) is an oversimplification. Although clear diagnostic boundaries remain use...

Clinical Abbreviation Disambiguation Using Deep Contextualized Representation.

Studies in health technology and informatics
The objective of this study is to develop a method for clinical abbreviation disambiguation using deep contextualized representation and cluster analysis. We employed the pre-trained BioELMo language model to generate the contextualized word vector f...

Character-Level Neural Language Modelling in the Clinical Domain.

Studies in health technology and informatics
Word embeddings have become the predominant representation scheme on a token-level for various clinical natural language processing (NLP) tasks. More recently, character-level neural language models, exploiting recurrent neural networks, have again r...

A multitask multiple kernel learning formulation for discriminating early- and late-stage cancers.

Bioinformatics (Oxford, England)
MOTIVATION: Genomic information is increasingly being used in diagnosis, prognosis and treatment of cancer. The severity of the disease is usually measured by the tumor stage. Therefore, identifying pathways playing an important role in progression o...

GSOAP: a tool for visualization of gene set over-representation analysis.

Bioinformatics (Oxford, England)
MOTIVATION: Gene sets over-representation analysis (GSOA) is a common technique of enrichment analysis that measures the overlap between a gene set and selected instances (e.g. pathways). Despite its popularity, there is currently no established stan...

Deep-learning approach to identifying cancer subtypes using high-dimensional genomic data.

Bioinformatics (Oxford, England)
MOTIVATION: Cancer subtype classification has the potential to significantly improve disease prognosis and develop individualized patient management. Existing methods are limited by their ability to handle extremely high-dimensional data and by the i...

Antibody Clustering Using a Machine Learning Pipeline that Fuses Genetic, Structural, and Physicochemical Properties.

Advances in experimental medicine and biology
Antibody V domain clustering is of paramount importance to a repertoire of immunology-related areas. Although several approaches have been proposed for antibody clustering, still no consensus has been reached. Numerous attempts use information from g...