AI Medical Compendium Topic:
Mutation

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A critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation.

Briefings in bioinformatics
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...

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
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...

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
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...

Unsupervised machine learning and prognostic factors of survival in chronic lymphocytic leukemia.

Journal of the American Medical Informatics Association : JAMIA
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...

Prediction of mutation effects using a deep temporal convolutional network.

Bioinformatics (Oxford, England)
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...

Automated machine learning based on radiomics features predicts H3 K27M mutation in midline gliomas of the brain.

Neuro-oncology
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...

A reference library for assigning protein subcellular localizations by image-based machine learning.

The Journal of cell biology
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...

[Prediction of gene mutation in lung cancer based on deep learning and histomorphology analysis].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Lung cancer is a most common malignant tumor of the lung and is the cancer with the highest morbidity and mortality worldwide. For patients with advanced non-small cell lung cancer who have undergone epidermal growth factor receptor (EGFR) gene mutat...

SomaticSeq: An Ensemble and Machine Learning Method to Detect Somatic Mutations.

Methods in molecular biology (Clifton, N.J.)
A standard strategy to discover somatic mutations in a cancer genome is to use next-generation sequencing (NGS) technologies to sequence the tumor tissue and its matched normal (commonly blood or adjacent normal tissue) for side-by-side comparison. H...

Ensemble-Based Somatic Mutation Calling in Cancer Genomes.

Methods in molecular biology (Clifton, N.J.)
Identification of somatic mutations in tumor tissue is challenged by both technical artifacts, diverse somatic mutational processes, and genetic heterogeneity in the tumors. Indeed, recent independent benchmark studies have revealed low concordance b...