AI Medical Compendium Topic:
Mutation

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Fully automated hybrid approach to predict the IDH mutation status of gliomas via deep learning and radiomics.

Neuro-oncology
BACKGROUND: Glioma prognosis depends on isocitrate dehydrogenase (IDH) mutation status. We aimed to predict the IDH status of gliomas from preoperative MR images using a fully automated hybrid approach with convolutional neural networks (CNNs) and ra...

On the critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation.

Briefings in bioinformatics
A review, recently published in this journal by Fang (2019), showed that methods trained for the prediction of protein stability changes upon mutation have a very critical bias: they neglect that a protein variation (A- > B) and its reverse (B- > A) ...

Machine learning demonstrates that somatic mutations imprint invariant morphologic features in myelodysplastic syndromes.

Blood
Morphologic interpretation is the standard in diagnosing myelodysplastic syndrome (MDS), but it has limitations, such as varying reliability in pathologic evaluation and lack of integration with genetic data. Somatic events shape morphologic features...

[Prevalence of transmitted drug resistance in HIV-infected treatment-naive patients in Chile].

Revista medica de Chile
BACKGROUND: Transmitted drug resistance (TDR) occurs in patients with HIV infection who are not exposed to antiretroviral drugs but who are infected with a virus with mutations associated with resistance.

Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning.

World journal of gastroenterology
BACKGROUND: Identifying genetic mutations in cancer patients have been increasingly important because distinctive mutational patterns can be very informative to determine the optimal therapeutic strategy. Recent studies have shown that deep learning-...

Prediction of survival rate and effect of drugs on cancer patients with somatic mutations of genes: An AI-based approach.

Chemical biology & drug design
The causal role of somatic mutation and its interrelationship with gene expression profile during tumor development has already been observed, which plays a major role to decide the cancer grades and overall survival. Accurate and robust prediction o...

TMLRpred: A machine learning classification model to distinguish reversible EGFR double mutant inhibitors.

Chemical biology & drug design
The EGFR is a clinically important therapeutic drug target in lung cancer. The first-generation tyrosine kinase inhibitors used in clinics are effective against L858R-mutated EGFR. However, relapse of the disease due to the presence of resistant muta...

Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms.

Molecular imaging and biology
PURPOSE: Considerable progress has been made in the assessment and management of non-small cell lung cancer (NSCLC) patients based on mutation status in the epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene (KRAS). At the...

DeepCLIP: predicting the effect of mutations on protein-RNA binding with deep learning.

Nucleic acids research
Nucleotide variants can cause functional changes by altering protein-RNA binding in various ways that are not easy to predict. This can affect processes such as splicing, nuclear shuttling, and stability of the transcript. Therefore, correct modeling...