AI Medical Compendium Topic:
Cluster Analysis

Clear Filters Showing 791 to 800 of 1324 articles

Natural language processing of Reddit data to evaluate dermatology patient experiences and therapeutics.

Journal of the American Academy of Dermatology
BACKGROUND: There is a lack of research studying patient-generated data on Reddit, one of the world's most popular forums with active users interested in dermatology. Techniques within natural language processing, a field of artificial intelligence, ...

Multivariate Cluster-Based Multifactor Dimensionality Reduction to Identify Genetic Interactions for Multiple Quantitative Phenotypes.

BioMed research international
To understand the pathophysiology of complex diseases, including hypertension, diabetes, and autism, deleterious phenotypes are unlikely due to the effects of single genes, but rather, gene-gene interactions (GGIs), which are widely analyzed by multi...

Deconvolution of autoencoders to learn biological regulatory modules from single cell mRNA sequencing data.

BMC bioinformatics
BACKGROUND: Unsupervised machine learning methods (deep learning) have shown their usefulness with noisy single cell mRNA-sequencing data (scRNA-seq), where the models generalize well, despite the zero-inflation of the data. A class of neural network...

SmartPulse, a machine learning approach for calibration-free dynamic RF shimming: Preliminary study in a clinical environment.

Magnetic resonance in medicine
PURPOSE: A calibration-free pulse design method is introduced to alleviate artifacts in clinical routine with parallel transmission at high field, dealing with significant inter-subject variability, found for instance in the abdomen.

FunSet: an open-source software and web server for performing and displaying Gene Ontology enrichment analysis.

BMC bioinformatics
BACKGROUND: Gene Ontology enrichment analysis provides an effective way to extract meaningful information from complex biological datasets. By identifying terms that are significantly overrepresented in a gene set, researchers can uncover biological ...

AutoCryoPicker: an unsupervised learning approach for fully automated single particle picking in Cryo-EM images.

BMC bioinformatics
BACKGROUND: An important task of macromolecular structure determination by cryo-electron microscopy (cryo-EM) is the identification of single particles in micrographs (particle picking). Due to the necessity of human involvement in the process, curre...

A review on brain tumor segmentation of MRI images.

Magnetic resonance imaging
The process of segmenting tumor from MRI image of a brain is one of the highly focused areas in the community of medical science as MRI is noninvasive imaging. This paper discusses a thorough literature review of recent methods of brain tumor segment...

Combining gene ontology with deep neural networks to enhance the clustering of single cell RNA-Seq data.

BMC bioinformatics
BACKGROUND: Single cell RNA sequencing (scRNA-seq) is applied to assay the individual transcriptomes of large numbers of cells. The gene expression at single-cell level provides an opportunity for better understanding of cell function and new discove...

Insights and approaches using deep learning to classify wildlife.

Scientific reports
The implementation of intelligent software to identify and classify objects and individuals in visual fields is a technology of growing importance to operatives in many fields, including wildlife conservation and management. To non-experts, the metho...