AI Medical Compendium Topic

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A New Graph Autoencoder-Based Consensus-Guided Model for scRNA-seq Cell Type Detection.

IEEE transactions on neural networks and learning systems
Single-cell RNA sequencing (scRNA-seq) technology is famous for providing a microscopic view to help capture cellular heterogeneity. This characteristic has advanced the field of genomics by enabling the delicate differentiation of cell types. Howeve...

A quantum-based oversampling method for classification of highly imbalanced and overlapped data.

Experimental biology and medicine (Maywood, N.J.)
Data imbalance is a challenging problem in classification tasks, and when combined with class overlapping, it further deteriorates classification performance. However, existing studies have rarely addressed both issues simultaneously. In this article...

DEPICTER: Deep representation clustering for histology annotation.

Computers in biology and medicine
Automatic segmentation of histopathology whole-slide images (WSI) usually involves supervised training of deep learning models with pixel-level labels to classify each pixel of the WSI into tissue regions such as benign or cancerous. However, fully s...

Cellograph: a semi-supervised approach to analyzing multi-condition single-cell RNA-sequencing data using graph neural networks.

BMC bioinformatics
With the growing number of single-cell datasets collected under more complex experimental conditions, there is an opportunity to leverage single-cell variability to reveal deeper insights into how cells respond to perturbations. Many existing approac...

LPI-SKMSC: Predicting LncRNA-Protein Interactions with Segmented k-mer Frequencies and Multi-space Clustering.

Interdisciplinary sciences, computational life sciences
 Long noncoding RNAs (lncRNAs) have significant regulatory roles in gene expression. Interactions with proteins are one of the ways lncRNAs play their roles. Since experiments to determine lncRNA-protein interactions (LPIs) are expensive and time-con...

Density clustering-based automatic anatomical section recognition in colonoscopy video using deep learning.

Scientific reports
Recognizing anatomical sections during colonoscopy is crucial for diagnosing colonic diseases and generating accurate reports. While recent studies have endeavored to identify anatomical regions of the colon using deep learning, the deformable anatom...

Contrastive and adversarial regularized multi-level representation learning for incomplete multi-view clustering.

Neural networks : the official journal of the International Neural Network Society
Incomplete multi-view clustering is a significant task in machine learning, given that complex systems in nature and society cannot be fully observed; it provides an opportunity to exploit the structure and functions of underlying systems. Current al...

Unsupervised and supervised discovery of tissue cellular neighborhoods from cell phenotypes.

Nature methods
It is poorly understood how different cells in a tissue organize themselves to support tissue functions. We describe the CytoCommunity algorithm for the identification of tissue cellular neighborhoods (TCNs) based on cell phenotypes and their spatial...

Time-Course Transcriptome Analysis Reveals Distinct Phases and Identifies Two Key Genes during Severe Fever with Thrombocytopenia Syndrome Virus Infection in PMA-Induced THP-1 Cells.

Viruses
In recent years, there have been significant advancements in the research of Severe Fever with Thrombocytopenia Syndrome Virus (SFTSV). However, several limitations and challenges still exist. For instance, researchers face constraints regarding expe...

scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles.

Genome biology
Many deep learning-based methods have been proposed to handle complex single-cell data. Deep learning approaches may also prove useful to jointly analyze single-cell RNA sequencing (scRNA-seq) and single-cell T cell receptor sequencing (scTCR-seq) da...