AI Medical Compendium Topic

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Exome Sequencing

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Artificial intelligence (AI)-assisted exome reanalysis greatly aids in the identification of new positive cases and reduces analysis time in a clinical diagnostic laboratory.

Genetics in medicine : official journal of the American College of Medical Genetics
PURPOSE: Artificial intelligence (AI) and variant prioritization tools for genomic variant analysis are being rapidly developed for use in clinical diagnostic testing. However, their clinical utility and reliability are currently limited. Therefore, ...

Sparsely Connected Autoencoders: A Multi-Purpose Tool for Single Cell omics Analysis.

International journal of molecular sciences
BACKGROUND: Biological processes are based on complex networks of cells and molecules. Single cell multi-omics is a new tool aiming to provide new incites in the complex network of events controlling the functionality of the cell.

DNN-Boost: Somatic mutation identification of tumor-only whole-exome sequencing data using deep neural network and XGBoost.

Journal of bioinformatics and computational biology
Detection of somatic mutation in whole-exome sequencing data can help elucidate the mechanism of tumor progression. Most computational approaches require exome sequencing for both tumor and normal samples. However, it is more common to sequence exome...

Deep learning using bulk RNA-seq data expands cell landscape identification in tumor microenvironment.

Oncoimmunology
The tumor microenvironment (TME) profoundly influences tumor progression and affects immunotherapy responses and resistance. Understanding its heterogeneity is the key for developing immunotherapy. However, the available methods can only partially po...

Universal prediction of cell-cycle position using transfer learning.

Genome biology
BACKGROUND: The cell cycle is a highly conserved, continuous process which controls faithful replication and division of cells. Single-cell technologies have enabled increasingly precise measurements of the cell cycle both as a biological process of ...

PhenoApt leverages clinical expertise to prioritize candidate genes via machine learning.

American journal of human genetics
In recent years, exome sequencing (ES) has shown great utility in the diagnoses of Mendelian disorders. However, after rigorous filtering, a typical ES analysis still involves the interpretation of hundreds of variants, which greatly hinders the rapi...

A copula based topology preserving graph convolution network for clustering of single-cell RNA-seq data.

PLoS computational biology
Annotation of cells in single-cell clustering requires a homogeneous grouping of cell populations. There are various issues in single cell sequencing that effect homogeneous grouping (clustering) of cells, such as small amount of starting RNA, limite...

A robust and scalable graph neural network for accurate single-cell classification.

Briefings in bioinformatics
Single-cell RNA sequencing (scRNA-seq) techniques provide high-resolution data on cellular heterogeneity in diverse tissues, and a critical step for the data analysis is cell type identification. Traditional methods usually cluster the cells and manu...

One Cell At a Time (OCAT): a unified framework to integrate and analyze single-cell RNA-seq data.

Genome biology
Integrative analysis of large-scale single-cell RNA sequencing (scRNA-seq) datasets can aggregate complementary biological information from different datasets. However, most existing methods fail to efficiently integrate multiple large-scale scRNA-se...

Recommendations for next generation sequencing data reanalysis of unsolved cases with suspected Mendelian disorders: A systematic review and meta-analysis.

Genetics in medicine : official journal of the American College of Medical Genetics
PURPOSE: The study aimed to determine the diagnostic yield, optimal timing, and methodology of next generation sequencing data reanalysis in suspected Mendelian disorders.