AIMC Topic: Exome

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Deep convolutional neural networks for accurate somatic mutation detection.

Nature communications Mar 4, 2019
Accurate detection of somatic mutations is still a challenge in cancer analysis. Here we present NeuSomatic, the first convolutional neural network approach for somatic mutation detection, which significantly outperforms previous methods on different...
Neural Networks, Computer Machine Learning Neoplasms Computational Biology Humans DNA Mutational Analysis Sequence Alignment Genes, Neoplasm Exome Databases, Genetic Sequence Analysis, DNA Mutation Diploidy
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DeepPVP: phenotype-based prioritization of causative variants using deep learning.

BMC bioinformatics Feb 6, 2019
BACKGROUND: Prioritization of variants in personal genomic data is a major challenge. Recently, computational methods that rely on comparing phenotype similarity have shown to be useful to identify causative variants. In these methods, pathogenicity ...
Exome Sequencing Exome Humans Neural Networks, Computer Software Genetic Variation Gene Frequency Phenotype Deep Learning
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Xrare: a machine learning method jointly modeling phenotypes and genetic evidence for rare disease diagnosis.

Genetics in medicine : official journal of the American College of Medical Genetics Jan 24, 2019
PURPOSE: Despite the successful progress next-generation sequencing technologies has achieved in diagnosing the genetic cause of rare Mendelian diseases, the current diagnostic rate is still far from satisfactory because of heterogeneity, imprecision...
High-Throughput Nucleotide Sequencing Rare Diseases Exome Mutation Genetic Testing Genetic Variation Humans Machine Learning Software Genotype Computational Biology Phenotype Genomics
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Integrative analysis and machine learning on cancer genomics data using the Cancer Systems Biology Database (CancerSysDB).

BMC bioinformatics Apr 24, 2018
BACKGROUND: Recent cancer genome studies on many human cancer types have relied on multiple molecular high-throughput technologies. Given the vast amount of data that has been generated, there are surprisingly few databases which facilitate access to...
Humans Neoplasms Systems Biology Machine Learning Genomics Exome Software Databases, Factual
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Generalising better: Applying deep learning to integrate deleteriousness prediction scores for whole-exome SNV studies.

PloS one Mar 14, 2018
Many automatic classifiers were introduced to aid inference of phenotypical effects of uncategorised nsSNVs (nonsynonymous Single Nucleotide Variations) in theoretical and medical applications. Lately, several meta-estimators have been proposed that ...
Machine Learning Polymorphism, Single Nucleotide Models, Genetic Exome Sequence Analysis, DNA Internet
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Who's Who? Detecting and Resolving Sample Anomalies in Human DNA Sequencing Studies with Peddy.

American journal of human genetics Feb 9, 2017
The potential for genetic discovery in human DNA sequencing studies is greatly diminished if DNA samples from a cohort are mislabeled, swapped, or contaminated or if they include unintended individuals. Unfortunately, the potential for such errors is...
Genome, Human Pedigree Exome Chromosomes, Human, X Female Genetic Association Studies Male Humans Polymorphism, Single Nucleotide Sequence Analysis, DNA Machine Learning Genotype Genetic Loci Software
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OVA: integrating molecular and physical phenotype data from multiple biomedical domain ontologies with variant filtering for enhanced variant prioritization.

Bioinformatics (Oxford, England) Aug 12, 2015
MOTIVATION: Exome sequencing has become a de facto standard method for Mendelian disease gene discovery in recent years, yet identifying disease-causing mutations among thousands of candidate variants remains a non-trivial task.
Computational Biology Biological Ontologies Disease Exome Transcriptome Algorithms Polymorphism, Single Nucleotide Phenotype Genotype Humans
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PaPI: pseudo amino acid composition to score human protein-coding variants.

BMC bioinformatics Apr 19, 2015
BACKGROUND: High throughput sequencing technologies are able to identify the whole genomic variation of an individual. Gene-targeted and whole-exome experiments are mainly focused on coding sequence variants related to a single or multiple nucleotide...
Genetic Variation High-Throughput Nucleotide Sequencing Genome, Human Algorithms Exome Humans Proteins Artificial Intelligence Amino Acids Software
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Mantis-ml: Disease-Agnostic Gene Prioritization from High-Throughput Genomic Screens by Stochastic Semi-supervised Learning.

American journal of human genetics May 7, 2020
Access to large-scale genomics datasets has increased the utility of hypothesis-free genome-wide analyses. However, gene signals are often insufficiently powered to reach experiment-wide significance, triggering a process of laborious triaging of gen...
Mice Genomics Genetic Association Studies Renal Insufficiency, Chronic Animals Amyotrophic Lateral Sclerosis Neural Networks, Computer Humans Deep Learning Supervised Machine Learning Disease Models, Animal Reproducibility of Results ROC Curve Stochastic Processes Area Under Curve Exome Epilepsy
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Encoding Clinical Data with the Human Phenotype Ontology for Computational Differential Diagnostics.

Current protocols in human genetics Sep 1, 2019
The Human Phenotype Ontology (HPO) is a standardized set of phenotypic terms that are organized in a hierarchical fashion. It is a widely used resource for capturing human disease phenotypes for computational analysis to support differential diagnost...
Whole Genome Sequencing Biological Ontologies Genetic Diseases, Inborn Exome Humans Software Phenotype Databases, Genetic Computational Biology Diagnosis, Differential
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