AIMC Topic: Mutation

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TMLRpred: A machine learning classification model to distinguish reversible EGFR double mutant inhibitors.

Chemical biology & drug design Sep 1, 2020
The EGFR is a clinically important therapeutic drug target in lung cancer. The first-generation tyrosine kinase inhibitors used in clinics are effective against L858R-mutated EGFR. However, relapse of the disease due to the presence of resistant muta...
Models, Theoretical Humans Mutation Machine Learning Datasets as Topic ErbB Receptors
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Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms.

Molecular imaging and biology Aug 1, 2020
PURPOSE: Considerable progress has been made in the assessment and management of non-small cell lung cancer (NSCLC) patients based on mutation status in the epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene (KRAS). At the...
Positron-Emission Tomography High-Throughput Nucleotide Sequencing Multivariate Analysis Proto-Oncogene Proteins p21(ras) ErbB Receptors Algorithms Female Genomics Aged Multimodal Imaging Humans Carcinoma, Non-Small-Cell Lung Area Under Curve Machine Learning Male Mutation Lung Neoplasms
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DeepCLIP: predicting the effect of mutations on protein-RNA binding with deep learning.

Nucleic acids research Jul 27, 2020
Nucleotide variants can cause functional changes by altering protein-RNA binding in various ways that are not easy to predict. This can affect processes such as splicing, nuclear shuttling, and stability of the transcript. Therefore, correct modeling...
Base Sequence Nucleic Acid Conformation Binding Sites Nucleotide Motifs RNA-Binding Proteins Mutation RNA Protein Binding Mice Computational Biology Animals Deep Learning Humans Computer Simulation
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A critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation.

Briefings in bioinformatics Jul 15, 2020
A number of machine learning (ML)-based algorithms have been proposed for predicting mutation-induced stability changes in proteins. In this critical review, we used hypothetical reverse mutations to evaluate the performance of five representative al...
Machine Learning Protein Stability Algorithms Proteins Mutation
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Predicting the pathogenicity of protein coding mutations using Natural Language Processing.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
DNA-Sequencing of tumor cells has revealed thousands of genetic mutations. However, cancer is caused by only some of them. Identifying mutations that contribute to tumor growth from neutral ones is extremely challenging and is currently carried out m...
Machine Learning Proteins Natural Language Processing Mutation Virulence
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Evaluating the Predictability of Cancer Types from 536 Somatic Mutations: A New Dataset.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
In this paper, we introduce a new dataset for cancer research containing somatic mutation states of 536 genes of the Cancer Gene Census (CGC). We used somatic mutation information from the Cancer Genome Atlas (TCGA) projects to create this dataset. A...
Sensitivity and Specificity Neoplasms Mutation Humans Neural Networks, Computer Machine Learning
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Unsupervised machine learning and prognostic factors of survival in chronic lymphocytic leukemia.

Journal of the American Medical Informatics Association : JAMIA Jul 1, 2020
OBJECTIVE: Unsupervised machine learning approaches hold promise for large-scale clinical data. However, the heterogeneity of clinical data raises new methodological challenges in feature selection, choosing a distance metric that captures biological...
Leukemia, Lymphocytic, Chronic, B-Cell Male Middle Aged Aged Aged, 80 and over Adult Kaplan-Meier Estimate ZAP-70 Protein-Tyrosine Kinase Immunoglobulin Heavy Chains Unsupervised Machine Learning Mutation Proportional Hazards Models Prognosis Female Humans
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Prediction of mutation effects using a deep temporal convolutional network.

Bioinformatics (Oxford, England) Apr 1, 2020
MOTIVATION: Accurate prediction of the effects of genetic variation is a major goal in biological research. Towards this goal, numerous machine learning models have been developed to learn information from evolutionary sequence data. The most effecti...
Neural Networks, Computer Machine Learning Software Mutation Sequence Alignment
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Automated machine learning based on radiomics features predicts H3 K27M mutation in midline gliomas of the brain.

Neuro-oncology Mar 5, 2020
BACKGROUND: Conventional MRI cannot be used to identify H3 K27M mutation status. This study aimed to investigate the feasibility of predicting H3 K27M mutation status by applying an automated machine learning (autoML) approach to the MR radiomics fea...
Humans Machine Learning Retrospective Studies Magnetic Resonance Imaging Brain Neoplasms Child, Preschool Female Mutation Male Middle Aged Child Young Adult Histones Adolescent Glioma Adult
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A reference library for assigning protein subcellular localizations by image-based machine learning.

The Journal of cell biology Mar 2, 2020
Confocal micrographs of EGFP fusion proteins localized at key cell organelles in murine and human cells were acquired for use as subcellular localization landmarks. For each of the respective 789,011 and 523,319 optically validated cell images, morph...
Cell Line Recombinant Fusion Proteins Image Processing, Computer-Assisted Protein Transport Secretory Pathway Epithelial Cells Pattern Recognition, Automated Mice Green Fluorescent Proteins Organelles Mutation Reference Standards Microscopy, Confocal Humans Machine Learning Animals
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