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Mutation

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High-throughput deep learning variant effect prediction with Sequence UNET.

Genome biology
Understanding coding mutations is important for many applications in biology and medicine but the vast mutation space makes comprehensive experimental characterisation impossible. Current predictors are often computationally intensive and difficult t...

Preliminary evaluation of deep learning for first-line diagnostic prediction of tumor mutational status.

Scientific reports
The detection of tumour gene mutations by DNA or RNA sequencing is crucial for the prescription of effective targeted therapies. Recent developments showed promising results for tumoral mutational status prediction using new deep learning based metho...

Histopathological bladder cancer gene mutation prediction with hierarchical deep multiple-instance learning.

Medical image analysis
Gene mutation detection is usually carried out by molecular biological methods, which is expensive and has a long-time cycle. In contrast, pathological images are ubiquitous. If clinically significant gene mutations can be predicted only through path...

Automatic extraction of ranked SNP-phenotype associations from text using a BERT-LSTM-based method.

BMC bioinformatics
Extraction of associations of singular nucleotide polymorphism (SNP) and phenotypes from biomedical literature is a vital task in BioNLP. Recently, some methods have been developed to extract mutation-diseases affiliations. However, no accessible met...

Predicting mutational function using machine learning.

Mutation research. Reviews in mutation research
Genetic variations are one of the major causes of phenotypic variations between human individuals. Although beneficial as being the substrate of evolution, germline mutations may cause diseases, including Mendelian diseases and complex diseases such ...

MRI-based deep learning techniques for the prediction of isocitrate dehydrogenase and 1p/19q status in grade 2-4 adult gliomas.

Journal of medical imaging and radiation oncology
Molecular biomarkers are becoming increasingly important in the classification of intracranial gliomas. While tissue sampling remains the gold standard, there is growing interest in the use of deep learning (DL) techniques to predict these markers. T...

Deep Learning Radiomics for the Assessment of Telomerase Reverse Transcriptase Promoter Mutation Status in Patients With Glioblastoma Using Multiparametric MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Studies have shown that magnetic resonance imaging (MRI)-based deep learning radiomics (DLR) has the potential to assess glioma grade; however, its role in predicting telomerase reverse transcriptase (TERT) promoter mutation status in pat...

Deep Learning Prediction of Promoter Mutation Status in Thyroid Cancer Using Histologic Images.

Medicina (Kaunas, Lithuania)
objectives: Telomerase reverse transcriptase () promoter mutation, found in a subset of patients with thyroid cancer, is strongly associated with aggressive biologic behavior. Predicting promoter mutation is thus necessary for the prognostic strati...

Accelerated Aging in LMNA Mutations Detected by Artificial Intelligence ECG-Derived Age.

Mayo Clinic proceedings
OBJECTIVE: To demonstrate early aging in patients with lamin A/C (LMNA) gene mutations after hypothesizing that they have a biological age older than chronological age, as such a finding impacts care.

Accurate stratification between VEXAS syndrome and differential diagnoses by deep learning analysis of peripheral blood smears.

Clinical chemistry and laboratory medicine
OBJECTIVES: VEXAS syndrome is a newly described autoinflammatory disease associated with somatic mutations and vacuolization of myeloid precursors. This disease possesses an increasingly broad spectrum, leading to an increase in the number of suspec...