AIMC Topic: Exome Sequencing

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Predicting high confidence ctDNA somatic variants with ensemble machine learning models.

Scientific reports
Circulating tumour DNA (ctDNA) is a minimally invasive cancer biomarker that can be used to inform treatment of cancer patients. The utility of ctDNA as a cancer biomarker depends on the ability to accurately detect somatic variants associated with c...

Predicting Diabetic Retinopathy Using a Machine Learning Approach Informed by Whole-Exome Sequencing Studies.

Biomedical and environmental sciences : BES
OBJECTIVE: To establish and validate a novel diabetic retinopathy (DR) risk-prediction model using a whole-exome sequencing (WES)-based machine learning (ML) method.

Machine learning to optimize automated RH genotyping using whole-exome sequencing data.

Blood advances
Rh phenotype matching reduces but does not eliminate alloimmunization in patients with sickle cell disease (SCD) due to RH genetic diversity that is not distinguishable by serological typing. RH genotype matching can potentially mitigate Rh alloimmun...

ABEILLE: a novel method for ABerrant Expression Identification empLoying machine LEarning from RNA-sequencing data.

Bioinformatics (Oxford, England)
MOTIVATION: Current advances in omics technologies are paving the diagnosis of rare diseases proposing a complementary assay to identify the responsible gene. The use of transcriptomic data to identify aberrant gene expression (AGE) has demonstrated ...

DeNovoCNN: a deep learning approach to de novo variant calling in next generation sequencing data.

Nucleic acids research
De novo mutations (DNMs) are an important cause of genetic disorders. The accurate identification of DNMs from sequencing data is therefore fundamental to rare disease research and diagnostics. Unfortunately, identifying reliable DNMs remains a major...

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...

HDMC: a novel deep learning-based framework for removing batch effects in single-cell RNA-seq data.

Bioinformatics (Oxford, England)
MOTIVATION: With the development of single-cell RNA sequencing (scRNA-seq) techniques, increasingly more large-scale gene expression datasets become available. However, to analyze datasets produced by different experiments, batch effects among differ...

Integration and transfer learning of single-cell transcriptomes via cFIT.

Proceedings of the National Academy of Sciences of the United States of America
Large, comprehensive collections of single-cell RNA sequencing (scRNA-seq) datasets have been generated that allow for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. As new methods...

Style transfer with variational autoencoders is a promising approach to RNA-Seq data harmonization and analysis.

Bioinformatics (Oxford, England)
MOTIVATION: The transcriptomic data are being frequently used in the research of biomarker genes of different diseases and biological states. The most common tasks there are the data harmonization and treatment outcome prediction. Both of them can be...

PhenoPro: a novel toolkit for assisting in the diagnosis of Mendelian disease.

Bioinformatics (Oxford, England)
MOTIVATION: Whole-exome sequencing (WES) is now being used in clinical practice for the diagnosis of the causal genes of Mendelian diseases. In order to make the diagnosis, however, the clinical phenotypes [e.g. Human Phenotype Ontology (HPO) terms] ...