AIMC Topic: Mutation

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Rapid intraoperative multi-molecular diagnosis of glioma with ultrasound radio frequency signals and deep learning.

EBioMedicine
BACKGROUND: Molecular diagnosis is crucial for biomarker-assisted glioma resection and management. However, some limitations of current molecular diagnostic techniques prevent their widespread use intraoperatively. With the unique advantages of ultra...

MEnTaT: A machine-learning approach for the identification of mutations to increase protein stability.

Proceedings of the National Academy of Sciences of the United States of America
Enhancing protein thermal stability is important for biomedical and industrial applications as well as in the research laboratory. Here, we describe a simple machine-learning method which identifies amino acid substitutions that contribute to thermal...

Geometric Graph Learning to Predict Changes in Binding Free Energy and Protein Thermodynamic Stability upon Mutation.

The journal of physical chemistry letters
Accurate prediction of binding free energy changes upon mutations is vital for optimizing drugs, designing proteins, understanding genetic diseases, and cost-effective virtual screening. While machine learning methods show promise in this domain, ach...

The Performance of Machine Learning for Prediction of H3K27 M Mutation in Midline Gliomas: A Systematic Review and Meta-Analysis.

World neurosurgery
BACKGROUND: Diffuse midline gliomas (DMGs) encompass a set of tumors, and those tumors with H3K27 M mutation carry a poor prognosis. In recent years, machine learning (ML)-based radiomics have shown promising results in predicting gene mutation statu...

Deep learning precipitation prediction models combined with feature analysis.

Environmental science and pollution research international
Precise rainfall forecasting modeling assumes a pivotal role in water resource planning and management. Conducting a comprehensive analysis of the rainfall time series and making appropriate adjustments to the forecast model settings based on the cha...

Predicting multiple conformations via sequence clustering and AlphaFold2.

Nature
AlphaFold2 (ref. ) has revolutionized structural biology by accurately predicting single structures of proteins. However, a protein's biological function often depends on multiple conformational substates, and disease-causing point mutations often ca...

The impact of misclassification errors on the performance of biomarkers based on next-generation sequencing, a simulation study.

Journal of biopharmaceutical statistics
The development of next-generation sequencing (NGS) opens opportunities for new applications such as liquid biopsy, in which tumor mutation genotypes can be determined by sequencing circulating tumor DNA after blood draws. However, with highly dilute...

Reliable interpretability of biology-inspired deep neural networks.

NPJ systems biology and applications
Deep neural networks display impressive performance but suffer from limited interpretability. Biology-inspired deep learning, where the architecture of the computational graph is based on biological knowledge, enables unique interpretability where re...

Patient Graph Deep Learning to Predict Breast Cancer Molecular Subtype.

IEEE/ACM transactions on computational biology and bioinformatics
Breast cancer is a heterogeneous disease consisting of a diverse set of genomic mutations and clinical characteristics. The molecular subtypes of breast cancer are closely tied to prognosis and therapeutic treatment options. We investigate using deep...

Embryonic cranial cartilage defects in the Fgfr3 mouse model of achondroplasia.

Anatomical record (Hoboken, N.J. : 2007)
Achondroplasia, the most common chondrodysplasia in humans, is caused by one of two gain of function mutations localized in the transmembrane domain of fibroblast growth factor receptor 3 (FGFR3) leading to constitutive activation of FGFR3 and subseq...