AIMC Topic: Mutation

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Deep representation learning improves prediction of LacI-mediated transcriptional repression.

Proceedings of the National Academy of Sciences of the United States of America Jul 6, 2021
Recent progress in DNA synthesis and sequencing technology has enabled systematic studies of protein function at a massive scale. We explore a deep mutational scanning study that measured the transcriptional repression function of 43,669 variants of ...
Reproducibility of Results Deep Learning Mutation Epistasis, Genetic Lac Repressors Protein Domains Escherichia coli Proteins Transcription, Genetic
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Editorial: Designing a Protocol Adopting an Artificial Intelligence (AI)-Driven Approach for Early Diagnosis of Late-Onset Alzheimer's Disease.

Journal of molecular neuroscience : MN Jul 1, 2021
Humans Ethnicity Early Diagnosis Hong Kong Patient Selection Asian People Alzheimer Disease Mental Status and Dementia Tests Late Onset Disorders Clinical Trial Protocols as Topic Wearable Electronic Devices Medical History Taking Memory Biomarkers Mobile Applications Behavior Language Cognition Case-Control Studies Observational Studies as Topic Mutation Models, Biological Artificial Intelligence Prospective Studies
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A novel machine learning approach (svmSomatic) to distinguish somatic and germline mutations using next-generation sequencing data.

Zoological research Mar 18, 2021
Somatic mutations are a large category of genetic variations, which play an essential role in tumorigenesis. Detection of somatic single nucleotide variants (SNVs) could facilitate downstream analysis of tumorigenesis. Many computational methods have...
Humans Machine Learning Computational Biology Mutation Gene Expression Regulation, Neoplastic Algorithms DNA Copy Number Variations High-Throughput Nucleotide Sequencing Animals Neoplasms
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Computational studies of anaplastic lymphoma kinase mutations reveal common mechanisms of oncogenic activation.

Proceedings of the National Academy of Sciences of the United States of America Mar 9, 2021
Kinases play important roles in diverse cellular processes, including signaling, differentiation, proliferation, and metabolism. They are frequently mutated in cancer and are the targets of a large number of specific inhibitors. Surveys of cancer gen...
Anaplastic Lymphoma Kinase Enzyme Activation Protein Conformation, alpha-Helical Humans Machine Learning Mutation Molecular Dynamics Simulation
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An explainable machine learning platform for pyrazinamide resistance prediction and genetic feature identification of Mycobacterium tuberculosis.

Journal of the American Medical Informatics Association : JAMIA Mar 1, 2021
OBJECTIVE: Tuberculosis is the leading cause of death from a single infectious agent. The emergence of antimicrobial resistant Mycobacterium tuberculosis strains makes the problem more severe. Pyrazinamide (PZA) is an important component for short-co...
Tuberculosis Mycobacterium tuberculosis Pyrazinamide Machine Learning Drug Resistance, Bacterial Microbial Sensitivity Tests Support Vector Machine Mutation Antitubercular Agents Humans Neural Networks, Computer
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IDH glioma radiogenomics in the era of deep learning.

Neuro-oncology Feb 25, 2021
Isocitrate Dehydrogenase Deep Learning Glioma Mutation Humans
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Fully automated hybrid approach to predict the IDH mutation status of gliomas via deep learning and radiomics.

Neuro-oncology Feb 25, 2021
BACKGROUND: Glioma prognosis depends on isocitrate dehydrogenase (IDH) mutation status. We aimed to predict the IDH status of gliomas from preoperative MR images using a fully automated hybrid approach with convolutional neural networks (CNNs) and ra...
Glioma Magnetic Resonance Imaging Mutation Isocitrate Dehydrogenase Retrospective Studies Brain Neoplasms Deep Learning Humans
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On the critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation.

Briefings in bioinformatics Jan 18, 2021
A review, recently published in this journal by Fang (2019), showed that methods trained for the prediction of protein stability changes upon mutation have a very critical bias: they neglect that a protein variation (A- > B) and its reverse (B- > A) ...
Machine Learning Algorithms Mutation Protein Stability
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Human and artificial intelligence to illuminate MDS.

Blood Nov 12, 2020
Humans Machine Learning Mutation Artificial Intelligence Myelodysplastic Syndromes
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Machine learning demonstrates that somatic mutations imprint invariant morphologic features in myelodysplastic syndromes.

Blood Nov 12, 2020
Morphologic interpretation is the standard in diagnosing myelodysplastic syndrome (MDS), but it has limitations, such as varying reliability in pathologic evaluation and lack of integration with genetic data. Somatic events shape morphologic features...
Myelodysplastic Syndromes Middle Aged Humans Aged Genetic Association Studies Machine Learning Adult Mutation Female Male
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