AIMC Topic: Mutation

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Image based cellular contractile force evaluation with small-world network inspired CNN: SW-UNet.

Biochemical and biophysical research communications Jul 7, 2020
We propose an image based cellular contractile force evaluation method using a machine learning technique. We use a special substrate that exhibits wrinkles when cells grab the substrate and contract, and the wrinkles can be used to visualize the for...
Microscopy Proto-Oncogene Proteins p21(ras) Image Processing, Computer-Assisted Osteosarcoma Bone Neoplasms Mutation Humans Neural Networks, Computer Machine Learning Algorithms Biomechanical Phenomena Cell Line, Tumor
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Metabolomics, machine learning and immunohistochemistry to predict succinate dehydrogenase mutational status in phaeochromocytomas and paragangliomas.

The Journal of pathology Jul 1, 2020
Phaeochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumours with a hereditary background in over one-third of patients. Mutations in succinate dehydrogenase (SDH) genes increase the risk for PPGLs and several other tumours. Mutation...
Humans Mutation Cohort Studies Immunohistochemistry Head and Neck Neoplasms Succinate Dehydrogenase Metabolomics Paraganglioma Adrenal Gland Neoplasms Pheochromocytoma Machine Learning
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Hereditary disease prediction in eukaryotic DNA: an adaptive signal processing approach.

Nucleosides, nucleotides & nucleic acids Jun 22, 2020
Hereditary disease prediction in eukaryotic DNA using signal processing approaches is an incredible work in bioinformatics. Researchers of various fields are trying to put forth a noninvasive approach to forecast the disease-related genes. As disease...
DNA Humans Mutation Neural Networks, Computer Eukaryota Algorithms Genetic Diseases, Inborn Computational Biology
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Rosetta custom score functions accurately predict ΔΔG of mutations at protein-protein interfaces using machine learning.

Chemical communications (Cambridge, England) May 22, 2020
Protein-protein interfaces play essential roles in a variety of biological processes and many therapeutic molecules are targeted at these interfaces. However, accurate predictions of the effects of interfacial mutations to identify "hotspots" have re...
Protein Interaction Domains and Motifs Machine Learning Proteins Mutation
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Drug Resistance Prediction Using Deep Learning Techniques on HIV-1 Sequence Data.

Viruses May 19, 2020
The fast replication rate and lack of repair mechanisms of human immunodeficiency virus (HIV) contribute to its high mutation frequency, with some mutations resulting in the evolution of resistance to antiretroviral therapies (ART). As such, studying...
Mutation Anti-Retroviral Agents HIV-1 Drug Resistance, Viral Deep Learning HIV Infections Genotype Databases, Genetic Humans Neural Networks, Computer Phylogeny
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CytoCensus, mapping cell identity and division in tissues and organs using machine learning.

eLife May 19, 2020
A major challenge in cell and developmental biology is the automated identification and quantitation of cells in complex multilayered tissues. We developed CytoCensus: an easily deployed implementation of supervised machine learning that extends conv...
Microscopy, Video Organoids Zebrafish Embryo, Mammalian Animals, Genetically Modified Automation Drosophila melanogaster Cell Division Tissue Culture Techniques Time-Lapse Imaging Image Processing, Computer-Assisted Mice Mutation Retina Animals Male Larva Time Factors Brain Female Reproducibility of Results Machine Learning Phenotype
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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).
Ex-Smokers Non-Smokers Carcinoembryonic Antigen Smokers Exome Sequencing Keratin-19 Lymphocytes, Tumor-Infiltrating Regression Analysis Mutation Forkhead Transcription Factors Adult Tumor Microenvironment Male Middle Aged Lung Neoplasms Antigens, Neoplasm B7-H1 Antigen Adenocarcinoma Carcinoma, Non-Small-Cell Lung Humans Machine Learning Female Carcinoma, Squamous Cell Aged Aged, 80 and over
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Artificial Intelligence and Machine learning based prediction of resistant and susceptible mutations in Mycobacterium tuberculosis.

Scientific reports Mar 26, 2020
Tuberculosis (TB), an infectious disease caused by Mycobacterium tuberculosis (M.tb), causes highest number of deaths globally for any bacterial disease necessitating novel diagnosis and treatment strategies. High-throughput sequencing methods genera...
Mycobacterium tuberculosis Genes, Bacterial Genes, MDR Bacterial Proteins Mutation Tuberculosis, Multidrug-Resistant Molecular Dynamics Simulation Humans Artificial Intelligence Machine Learning Algorithms Molecular Docking Simulation Drug Resistance, Multiple, Bacterial Polymorphism, Single Nucleotide Bayes Theorem
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DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure.

Genome biology Mar 26, 2020
Non-coding variants have been shown to be related to disease by alteration of 3D genome structures. We propose a deep learning method, DeepMILO, to predict the effects of variants on CTCF/cohesin-mediated insulator loops. Application of DeepMILO on v...
Chromatin CCCTC-Binding Factor Chromosomal Proteins, Non-Histone Insulator Elements Cohesins Cell Line, Tumor Mutation Whole Genome Sequencing Cell Cycle Proteins Humans Neoplasms Deep Learning
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Diagnostic accuracy and potential covariates for machine learning to identify IDH mutations in glioma patients: evidence from a meta-analysis.

European radiology Mar 19, 2020
OBJECTIVES: To assess the diagnostic accuracy of machine learning (ML) in predicting isocitrate dehydrogenase (IDH) mutations in patients with glioma and to identify potential covariates that could influence the diagnostic performance of ML.
Isocitrate Dehydrogenase Humans Glioma Magnetic Resonance Imaging Mutation DNA Mutational Analysis DNA, Neoplasm Machine Learning Biomarkers, Tumor Brain Neoplasms ROC Curve
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