AIMC Topic: Mutation
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Machine learning-informed liquid-liquid phase separation for personalized breast cancer treatment assessment.
Frontiers in immunology
Nov 19, 2024
BACKGROUND: Breast cancer, characterized by its heterogeneity, is a leading cause of mortality among women. The study aims to develop a Machine Learning-Derived Liquid-Liquid Phase Separation (MDLS) model to enhance the prognostic accuracy and person...
Machine learning-driven estimation of mutational burden highlights DNAH5 as a prognostic marker in colorectal cancer.
Biology direct
Nov 14, 2024
BACKGROUND: Tumor Mutational Burden (TMB) have emerged as pivotal predictive biomarkers in determining prognosis and response to immunotherapy in colorectal cancer (CRC) patients. While Whole Exome Sequencing (WES) stands as the gold standard for TMB...
Leveraging large-scale Mycobacterium tuberculosis whole genome sequence data to characterise drug-resistant mutations using machine learning and statistical approaches.
Scientific reports
Nov 7, 2024
Tuberculosis disease (TB), caused by Mycobacterium tuberculosis (Mtb), is a major global public health problem, resulting in > 1 million deaths each year. Drug resistance (DR), including the multi-drug form (MDR-TB), is challenging control of the dis...
Generative AI in the Advancement of Viral Therapeutics for Predicting and Targeting Immune-Evasive SARS-CoV-2 Mutations.
IEEE journal of biomedical and health informatics
Nov 6, 2024
The emergence of immune-evasive mutations in the SARS-CoV-2 spike protein is consistently challenging existing vaccines and therapies, making precise prediction of their escape potential a critical imperative. Artificial Intelligence(AI) holds great ...
Classifying driver mutations of papillary thyroid carcinoma on whole slide image: an automated workflow applying deep convolutional neural network.
Frontiers in endocrinology
Nov 6, 2024
BACKGROUND: Informative biomarkers play a vital role in guiding clinical decisions regarding management of cancers. We have previously demonstrated the potential of a deep convolutional neural network (CNN) for predicting cancer driver gene mutations...
An interpretable deep learning model for detecting pathogenic variants of breast cancer from hematoxylin and eosin-stained pathological images.
PeerJ
Oct 28, 2024
BACKGROUND: Determining the status of breast cancer susceptibility genes () is crucial for guiding breast cancer treatment. Nevertheless, the need for genetic testing among breast cancer patients remains unmet due to high costs and limited resources...
Ensemble transformer-based multiple instance learning to predict pathological subtypes and tumor mutational burden from histopathological whole slide images of endometrial and colorectal cancer.
Medical image analysis
Oct 21, 2024
In endometrial cancer (EC) and colorectal cancer (CRC), in addition to microsatellite instability, tumor mutational burden (TMB) has gradually gained attention as a genomic biomarker that can be used clinically to determine which patients may benefit...
Artificial intelligence can be trained to predict -11 mutational status of canine mast cell tumors from hematoxylin and eosin-stained histological slides.
Veterinary pathology
Oct 18, 2024
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
Oct 9, 2024
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
Sep 30, 2024
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...
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