AIMC Topic: Exome Sequencing

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Leveraging Whole-Exome Sequencing and Mutational Signatures to Detect Homologous Recombination Deficiency in Cancer.

Cancer research Jul 2, 2025
Homologous recombination is a high-fidelity DNA repair mechanism essential for maintaining genome stability. Impairment of this pathway, often due to BRCA1 or BRCA2 inactivation, leads to homologous recombination deficiency (HRD), forcing cells to re...
Neoplasms Humans Mutation Machine Learning Homologous Recombination Exome Sequencing
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HRProfiler Detects Homologous Recombination Deficiency in Breast and Ovarian Cancers Using Whole-Genome and Whole-Exome Sequencing Data.

Cancer research Jul 2, 2025
UNLABELLED: Breast and ovarian cancers harboring homologous recombination deficiency (HRD) are sensitive to PARP inhibitors and platinum chemotherapy. Conventionally, detecting HRD involves screening for defects in BRCA1, BRCA2, and other relevant ge...
Humans Female Breast Neoplasms Ovarian Neoplasms Exome Sequencing Machine Learning Poly(ADP-ribose) Polymerase Inhibitors Retrospective Studies Homologous Recombination Mutation Whole Genome Sequencing
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Predicting high confidence ctDNA somatic variants with ensemble machine learning models.

Scientific reports May 26, 2025
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...
Biomarkers, Tumor Neoplasms Mutation Humans Machine Learning Circulating Tumor DNA High-Throughput Nucleotide Sequencing Exome Sequencing
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Predicting Diabetic Retinopathy Using a Machine Learning Approach Informed by Whole-Exome Sequencing Studies.

Biomedical and environmental sciences : BES Jan 20, 2025
OBJECTIVE: To establish and validate a novel diabetic retinopathy (DR) risk-prediction model using a whole-exome sequencing (WES)-based machine learning (ML) method.
Mutation Pedigree Exome Sequencing Genetic Predisposition to Disease Polymorphism, Single Nucleotide Aged Adult Middle Aged Diabetes Mellitus, Type 2 Male Machine Learning Female Humans Diabetic Retinopathy
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Machine learning to optimize automated RH genotyping using whole-exome sequencing data.

Blood advances Jun 11, 2024
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...
Alleles Anemia, Sickle Cell Exome Sequencing Rh-Hr Blood-Group System Genotyping Techniques Humans Machine Learning Genotype
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ABEILLE: a novel method for ABerrant Expression Identification empLoying machine LEarning from RNA-sequencing data.

Bioinformatics (Oxford, England) Oct 14, 2022
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 ...
Software Sequence Analysis, RNA Machine Learning RNA Exome Sequencing
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DeNovoCNN: a deep learning approach to de novo variant calling in next generation sequencing data.

Nucleic acids research Sep 23, 2022
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...
High-Throughput Nucleotide Sequencing Exome Sequencing Deep Learning Sequence Analysis, DNA
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A robust and scalable graph neural network for accurate single-cell classification.

Briefings in bioinformatics Mar 10, 2022
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...
Sequence Analysis, RNA Exome Sequencing Algorithms Learning Single-Cell Analysis Neural Networks, Computer
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HDMC: a novel deep learning-based framework for removing batch effects in single-cell RNA-seq data.

Bioinformatics (Oxford, England) Feb 7, 2022
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...
Single-Cell Analysis Single-Cell Gene Expression Analysis Sequence Analysis, RNA Gene Expression Profiling Deep Learning Exome Sequencing
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Integration and transfer learning of single-cell transcriptomes via cFIT.

Proceedings of the National Academy of Sciences of the United States of America Mar 9, 2021
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...
Transcriptome Single-Cell Analysis Mice Exome Sequencing RNA-Seq Humans Software Machine Learning Animals
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