AIMC Topic: Mutation

Clear Filters Showing 171 to 180 of 726 articles

Artificial intelligence can be trained to predict -11 mutational status of canine mast cell tumors from hematoxylin and eosin-stained histological slides.

Veterinary pathology
Numerous prognostic factors are currently assessed histologically and immunohistochemically in canine mast cell tumors (MCTs) to evaluate clinical behavior. In addition, polymerase chain reaction (PCR) is often performed to detect internal tandem dup...

Analysis of Cancer-Associated Mutations of POLB Using Machine Learning and Bioinformatics.

IEEE/ACM transactions on computational biology and bioinformatics
DNA damage is a critical factor in the onset and progression of cancer. When DNA is damaged, the number of genetic mutations increases, making it necessary to activate DNA repair mechanisms. A crucial factor in the base excision repair process, which...

Deep learning for discriminating non-trivial conformational changes in molecular dynamics simulations of SARS-CoV-2 spike-ACE2.

Scientific reports
Molecular dynamics (MD) simulations produce a substantial volume of high-dimensional data, and traditional methods for analyzing these data pose significant computational demands. Advances in MD simulation analysis combined with deep learning-based a...

Machine learning optimized DriverDetect software for high precision prediction of deleterious mutations in human cancers.

Scientific reports
The detection of cancer-driving mutations is important for understanding cancer pathology and therapeutics development. Prediction tools have been created to streamline the computation process. However, most tools available have heterogeneous sensiti...

Deep Learning and Habitat Radiomics for the Prediction of Glioma Pathology Using Multiparametric MRI: A Multicenter Study.

Academic radiology
RATIONALE AND OBJECTIVES: Recent radiomics studies on predicting pathological outcomes of glioma have shown immense potential. However, the predictive ability remains suboptimal due to the tumor intrinsic heterogeneity. We aimed to achieve better pat...

Combining clinical and molecular data for personalized treatment in acute myeloid leukemia: A machine learning approach.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The standard of care in Acute Myeloid Leukemia patients has remained essentially unchanged for nearly 40 years. Due to the complicated mutational patterns within and between individual patients and a lack of targeted agents ...

The application value of support vector machine model based on multimodal MRI in predicting IDH-1mutation and Ki-67 expression in glioma.

BMC medical imaging
PURPOSE: To investigate the application value of support vector machine (SVM) model based on diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE) and amide proton transfer- weighted (APTW) imaging in predicting isocitrate dehydrogenase 1...

Deep Learning-Based Blood Abnormalities Detection as a Tool for VEXAS Syndrome Screening.

International journal of laboratory hematology
INTRODUCTION: VEXAS is a syndrome described in 2020, caused by mutations of the UBA1 gene, and displaying a large pleomorphic array of clinical and hematological features. Nevertheless, these criteria lack significance to discriminate VEXAS from othe...

Conformations of KRAS4B Affected by Its Partner Binding and G12C Mutation: Insights from GaMD Trajectory-Image Transformation-Based Deep Learning.

Journal of chemical information and modeling
Binding of partners and mutations highly affects the conformational dynamics of KRAS4B, which is of significance for deeply understanding its function. Gaussian accelerated molecular dynamics (GaMD) simulations followed by deep learning (DL) and prin...

An end-to-end framework for the prediction of protein structure and fitness from single sequence.

Nature communications
Significant research progress has been made in the field of protein structure and fitness prediction. Particularly, single-sequence-based structure prediction methods like ESMFold and OmegaFold achieve a balance between inference speed and prediction...