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Mutation

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Machine Learning Predictability of Clinical Next Generation Sequencing for Hematologic Malignancies to Guide High-Value Precision Medicine.

AMIA ... Annual Symposium proceedings. AMIA Symposium Feb 21, 2022
Advancing diagnostic testing capabilities such as clinical next generation sequencing methods offer the potential to diagnose, risk stratify, and guide specialized treatment, but must be balanced against the escalating costs of healthcare to identify...
Heme Precision Medicine High-Throughput Nucleotide Sequencing Hematologic Neoplasms Humans Mutation Machine Learning
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Accurate and rapid prediction of tuberculosis drug resistance from genome sequence data using traditional machine learning algorithms and CNN.

Scientific reports Feb 14, 2022
Effective and timely antibiotic treatment depends on accurate and rapid in silico antimicrobial-resistant (AMR) predictions. Existing statistical rule-based Mycobacterium tuberculosis (MTB) drug resistance prediction methods using bacterial genomic s...
Humans Phenotype Prognosis Mutation Microbial Sensitivity Tests Antitubercular Agents Data Accuracy Drug Resistance, Multiple, Bacterial Whole Genome Sequencing Genome, Bacterial Cohort Studies Phylogeny Mycobacterium tuberculosis Tuberculosis, Multidrug-Resistant Deep Learning
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Predicting EGFR mutation status by a deep learning approach in patients with non-small cell lung cancer brain metastases.

Journal of neuro-oncology Feb 4, 2022
PURPOSE: Non-small cell lung cancer (NSCLC) tends to metastasize to the brain. Between 10 and 60% of NSCLCs harbor an activating mutation in the epidermal growth-factor receptor (EGFR), which may be targeted with selective EGFR inhibitors. However, d...
Humans Prospective Studies Retrospective Studies Carcinoma, Non-Small-Cell Lung Deep Learning Brain Neoplasms Mutation Lung Neoplasms ErbB Receptors
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Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample.

Genome biology Jan 7, 2022
BACKGROUND: Accurate detection of somatic mutations is challenging but critical in understanding cancer formation, progression, and treatment. We recently proposed NeuSomatic, the first deep convolutional neural network-based somatic mutation detecti...
Deep Learning Genomics Mutation Neural Networks, Computer Neoplasms Humans
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Deep Unfolding for Non-Negative Matrix Factorization with Application to Mutational Signature Analysis.

Journal of computational biology : a journal of computational molecular cell biology Jan 5, 2022
Non-negative matrix factorization (NMF) is a fundamental matrix decomposition technique that is used primarily for dimensionality reduction and is increasing in popularity in the biological domain. Although finding a unique NMF is generally not possi...
Computational Biology Databases, Genetic Unsupervised Machine Learning Breast Neoplasms Deep Learning Supervised Machine Learning DNA Mutational Analysis Mutation Humans Neural Networks, Computer Computer Simulation Female Algorithms
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Combination of liquid crystal and deep learning reveals distinct signatures of Parkinson's disease-related wild-type α-synuclein and six pathogenic mutants.

Chemistry, an Asian journal Dec 21, 2021
α-Synuclein is a central player in Parkinson's disease (PD) pathology. Various point mutations in α-synuclein have been identified to alter the protein-phospholipid binding behavior and cause PD. Therefore, exploration of α-synuclein-phospholipid int...
Parkinson Disease Liquid Crystals Humans alpha-Synuclein Mutation Deep Learning
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Clinical feature-related single-base substitution sequence signatures identified with an unsupervised machine learning approach.

BMC medical genomics Dec 20, 2021
BACKGROUND: Mutation processes leave different signatures in genes. For single-base substitutions, previous studies have suggested that mutation signatures are not only reflected in mutation bases but also in neighboring bases. However, because of th...
Mutation, Missense Algorithms Unsupervised Machine Learning Cluster Analysis Mutation Humans
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Artificial intelligence challenges for predicting the impact of mutations on protein stability.

Current opinion in structural biology Dec 15, 2021
Stability is a key ingredient of protein fitness, and its modification through targeted mutations has applications in various fields, such as protein engineering, drug design, and deleterious variant interpretation. Many studies have been devoted ove...
Artificial Intelligence Machine Learning Protein Stability Algorithms Mutation
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DNN-Boost: Somatic mutation identification of tumor-only whole-exome sequencing data using deep neural network and XGBoost.

Journal of bioinformatics and computational biology Dec 13, 2021
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...
High-Throughput Nucleotide Sequencing Exome Sequencing Exome Neoplasms Humans Mutation Neural Networks, Computer
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Genome-Wide Mutation Scoring for Machine-Learning-Based Antimicrobial Resistance Prediction.

International journal of molecular sciences Dec 2, 2021
The prediction of antimicrobial resistance (AMR) based on genomic information can improve patient outcomes. Genetic mechanisms have been shown to explain AMR with accuracies in line with standard microbiology laboratory testing. To translate genetic ...
Machine Learning Drug Resistance, Bacterial Genomics Escherichia coli Anti-Infective Agents Pseudomonas aeruginosa Genome, Bacterial Mutation Whole Genome Sequencing
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