AIMC Topic: Mutation

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Machine Learning on the Impacts of Mutations in the SARS-CoV-2 Spike RBD on Binding Affinity to Human ACE2 Based on Deep Mutational Scanning Data.

Biochemistry
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to accumulate mutations in the spike receptor-binding domain (RBD) region, leading to the emergence of new variants that potentially change the binding affinity for the human angi...

TG-Mamba: Leveraging text guidance for predicting tumor mutation burden in lung cancer.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Tumor mutation burden (TMB) is a crucial biomarker for predicting the response of lung cancer patients to immunotherapy. Traditionally, TMB is quantified through whole-exome sequencing (WES), but the high costs and time requirements of WES limit its ...

Phenotype augmentation using generative AI for isocitrate dehydrogenase mutation prediction in glioma.

Scientific reports
This study investigated the effects of feature augmentation, which uses generated images with specific imaging features, on the performance of isocitrate dehydrogenase (IDH) mutation prediction models in gliomas. A total of 598 patients were included...

Advances in mass spectrometry of lipids for the investigation of Niemann-pick type C disease.

Lipids in health and disease
Niemann-Pick type C (NPC) disease is a devastating, fatal, neurodegenerative disease and a form of lysosomal storage disorder. It is caused by mutations in either NPC1 or NPC2 genes, leading to the accumulation of cholesterol and other lipids in the ...

A computational pipeline for predicting distal hotspots in an artificial enzyme.

International journal of biological macromolecules
Targeting distal mutations holds promising implications for enzyme engineering. Here, we present an open-source computational workflow designed to explore the functional impact of distal sites, demonstrated on an artificial enzyme built on the widely...

Machine learning-guided evolution of pyrrolysyl-tRNA synthetase for improved incorporation efficiency of diverse noncanonical amino acids.

Nature communications
The pyrrolysyl-tRNA synthetase (PylRS) is widely used to incorporate noncanonical amino acids (ncAAs) into proteins. However, the yields of most ncAA-containing protein  remain low due to the limited activity of PylRS variants. Here, we apply machine...

Performance of Machine Learning in Diagnosing KRAS (Kirsten Rat Sarcoma) Mutations in Colorectal Cancer: Systematic Review and Meta-Analysis.

Journal of medical Internet research
BACKGROUND: With the widespread application of machine learning (ML) in the diagnosis and treatment of colorectal cancer (CRC), some studies have investigated the use of ML techniques for the diagnosis of KRAS (Kirsten rat sarcoma) mutation. Neverthe...

Integrative scATAC-seq and mtDNA mutation analysis reveals disease-driven regulatory aberrations in AML.

Science bulletin
Disturbances in transcriptional regulation during hematopoiesis can lead to aberrant hematopoietic differentiation and potential leukemogenesis. Given the high relapse risk of acute myeloid leukemia (AML), understanding its progression mechanisms and...

Prediction of pathogenic mutations in human transmembrane proteins and their associated diseases via utilizing pre-trained Bio-LLMs.

Communications biology
Missense mutations can disrupt the structure and function of membrane proteins, potentially impairing key biological processes and leading to various human diseases. However, existing computational methods primarily focus on binary pathogenicity clas...

Predicting the molecular subtypes of 2021 WHO grade 4 glioma by a multiparametric MRI-based machine learning model.

BMC cancer
BACKGROUND: Accurately distinguishing the different molecular subtypes of 2021 World Health Organization (WHO) grade 4 Central Nervous System (CNS) gliomas is highly relevant for prognostic stratification and personalized treatment.