AIMC Topic: Exome Sequencing

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Semi-supervised learning for somatic variant calling and peptide identification in personalized cancer immunotherapy.

BMC bioinformatics Dec 30, 2020
BACKGROUND: Personalized cancer vaccines are emerging as one of the most promising approaches to immunotherapy of advanced cancers. However, only a small proportion of the neoepitopes generated by somatic DNA mutations in cancer cells lead to tumor r...
Humans Immunotherapy Epitopes Supervised Machine Learning Tandem Mass Spectrometry Precision Medicine Exome Sequencing Neoplasms Peptides Polymorphism, Single Nucleotide
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Deep learning predicts short non-coding RNA functions from only raw sequence data.

PLoS computational biology Nov 11, 2020
Small non-coding RNAs (ncRNAs) are short non-coding sequences involved in gene regulation in many biological processes and diseases. The lack of a complete comprehension of their biological functionality, especially in a genome-wide scenario, has dem...
Exome Sequencing RNA, Untranslated Databases, Nucleic Acid Humans Sequence Analysis, RNA Neural Networks, Computer High-Throughput Nucleotide Sequencing Deep Learning Nucleic Acid Conformation Computational Biology Monte Carlo Method
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Unsupervised Clustering of Missense Variants in HNF1A Using Multidimensional Functional Data Aids Clinical Interpretation.

American journal of human genetics Sep 9, 2020
Exome sequencing in diabetes presents a diagnostic challenge because depending on frequency, functional impact, and genomic and environmental contexts, HNF1A variants can cause maturity-onset diabetes of the young (MODY), increase type 2 diabetes ris...
Humans Male Adolescent Child Diabetes Mellitus, Type 2 Genetic Predisposition to Disease Phenotype Female Young Adult Adult United Kingdom Norway Exome Sequencing Mutation, Missense Gene Expression Registries Alleles Datasets as Topic Cluster Analysis Hepatocyte Nuclear Factor 1-alpha Unsupervised Machine Learning Principal Component Analysis
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Assessment of associations between clinical and immune microenvironmental factors and tumor mutation burden in resected nonsmall cell lung cancer by applying machine learning to whole-slide images.

Cancer medicine May 12, 2020
BACKGROUND: It is unclear whether clinical factors and immune microenvironment (IME) factors are associated with tumor mutation burden (TMB) in patients with nonsmall cell lung cancer (NSCLC).
Keratin-19 Ex-Smokers Non-Smokers Forkhead Transcription Factors Carcinoembryonic Antigen Smokers Exome Sequencing Lung Neoplasms B7-H1 Antigen Antigens, Neoplasm Adenocarcinoma Carcinoma, Non-Small-Cell Lung Lymphocytes, Tumor-Infiltrating Regression Analysis Mutation Adult Tumor Microenvironment Male Middle Aged Female Carcinoma, Squamous Cell Aged Aged, 80 and over Humans Machine Learning
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Machine Learning and Network Analyses Reveal Disease Subtypes of Pancreatic Cancer and their Molecular Characteristics.

Scientific reports Jan 27, 2020
Given that the biological processes governing the oncogenesis of pancreatic cancers could present useful therapeutic targets, there is a pressing need to molecularly distinguish between different clinically relevant pancreatic cancer subtypes. To add...
RNA, Messenger Proteome DNA Methylation MicroRNAs Transcription, Genetic Biomarkers, Tumor Humans Transcriptome Pancreatic Neoplasms Machine Learning Cell Line, Tumor Exome Sequencing RNA-Seq
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A machine-learning approach for accurate detection of copy number variants from exome sequencing.

Genome research Jun 6, 2019
Copy number variants (CNVs) are a major cause of several genetic disorders, making their detection an essential component of genetic analysis pipelines. Current methods for detecting CNVs from exome-sequencing data are limited by high false-positive ...
Humans Machine Learning Algorithms DNA Copy Number Variations Exome Sequencing Exome
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Application of a Neural Network Whole Transcriptome-Based Pan-Cancer Method for Diagnosis of Primary and Metastatic Cancers.

JAMA network open Apr 5, 2019
IMPORTANCE: A molecular diagnostic method that incorporates information about the transcriptional status of all genes across multiple tissue types can strengthen confidence in cancer diagnosis.
Lung Neoplasms Mesothelioma Mesothelioma, Malignant Exome Sequencing Male Middle Aged Neoplasms Transcriptome Female Adult Biomarkers, Tumor Cross-Sectional Studies Humans Neural Networks, Computer Machine Learning
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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 ...
Humans Neural Networks, Computer Software Exome Sequencing Exome Phenotype Deep Learning Genetic Variation Gene Frequency
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MSIpred: a python package for tumor microsatellite instability classification from tumor mutation annotation data using a support vector machine.

Scientific reports Dec 3, 2018
Microsatellite instability (MSI) is characterized by high degree of polymorphism in microsatellite lengths due to deficiency in mismatch repair (MMR) system. MSI is associated with several tumor types and its status can be considered as an important ...
Databases, Nucleic Acid Humans Programming Languages Neoplasms DNA, Neoplasm Support Vector Machine Exome Sequencing Microsatellite Instability Molecular Sequence Annotation
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A case of apolipoprotein A-I deficiency due to carboxyl-terminal truncation.

Journal of clinical lipidology Dec 29, 2017
Apolipoprotein A-I deficiency is a rare metabolic disease characterized by an impaired reverse cholesterol transport system resulting in excessive cholesterol accumulation. Here, we discuss a case of apolipoprotein A-I deficiency caused by a carboxyl...
Family Health Apolipoprotein A-I Base Sequence Pedigree Cholesterol Exome Sequencing Humans Cholesterol, HDL Female DNA Mutational Analysis Aged Mutation
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