AIMC Topic: Mutation
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Histopathological bladder cancer gene mutation prediction with hierarchical deep multiple-instance learning.
Medical image analysis
Apr 23, 2023
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
Apr 12, 2023
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
Mar 23, 2023
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
Mar 15, 2023
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
Mar 10, 2023
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)
Mar 9, 2023
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
Feb 11, 2023
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
Feb 2, 2023
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...
Using Haplotype-Based Artificial Intelligence to Evaluate SARS-CoV-2 Novel Variants and Mutations.
JAMA network open
Feb 1, 2023
IMPORTANCE: Earlier detection of emerging novel SARS-COV-2 variants is important for public health surveillance of potential viral threats and for earlier prevention research. Artificial intelligence may facilitate early detection of SARS-CoV2 emergi...
Annotation-Free Deep Learning-Based Prediction of Thyroid Molecular Cancer Biomarker BRAF (V600E) from Cytological Slides.
International journal of molecular sciences
Jan 28, 2023
Thyroid cancer is the most common endocrine cancer. Papillary thyroid cancer (PTC) is the most prevalent form of malignancy among all thyroid cancers arising from follicular cells. Fine needle aspiration cytology (FNAC) is a non-invasive method regar...
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