AIMC Topic: Mutation

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Code-free machine learning for classification of central nervous system histopathology images.

Journal of neuropathology and experimental neurology Feb 21, 2023
Machine learning (ML), an application of artificial intelligence, is currently transforming the analysis of biomedical data and specifically of biomedical images including histopathology. The promises of this technology contrast, however, with its cu...
Mutation Central Nervous System Isocitrate Dehydrogenase Humans Artificial Intelligence Machine Learning Brain Neoplasms
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Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning.

Neuro-oncology Feb 14, 2023
BACKGROUND: Accurate characterization of glioma is crucial for clinical decision making. A delineation of the tumor is also desirable in the initial decision stages but is time-consuming. Previously, deep learning methods have been developed that can...
Glioma Magnetic Resonance Imaging Brain Neoplasms Humans Neoplasm Grading Deep Learning Isocitrate Dehydrogenase Mutation Chromosome Aberrations
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Predicting colorectal cancer tumor mutational burden from histopathological images and clinical information using multi-modal deep learning.

Bioinformatics (Oxford, England) Nov 15, 2022
MOTIVATION: Tumor mutational burden (TMB) is an indicator of the efficacy and prognosis of immune checkpoint therapy in colorectal cancer (CRC). In general, patients with higher TMB values are more likely to benefit from immunotherapy. Though whole-e...
Humans Colorectal Neoplasms Immunotherapy Mutation Biomarkers, Tumor Deep Learning
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BayeStab: Predicting effects of mutations on protein stability with uncertainty quantification.

Protein science : a publication of the Protein Society Nov 1, 2022
Predicting protein thermostability change upon mutation is crucial for understanding diseases and designing therapeutics. However, accurately estimating Gibbs free energy change of the protein remained a challenge. Some methods struggle to generalize...
Protein Stability Uncertainty Neural Networks, Computer Bayes Theorem Mutation
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Investigating Morphologic Correlates of Driver Gene Mutation Heterogeneity via Deep Learning.

Cancer research Aug 3, 2022
Despite the crucial role of phenotypic and genetic intratumoral heterogeneity in understanding and predicting clinical outcomes for patients with cancer, computational pathology studies have yet to make substantial steps in this area. The major limit...
Deep Learning Mutation Humans Kidney Neoplasms
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An Active Learning Framework Improves Tumor Variant Interpretation.

Cancer research Aug 3, 2022
A novel machine learning approach predicts the impact of tumor mutations on cellular phenotypes, overcomes limited training data, minimizes costly functional validation, and advances efforts to implement cancer precision medicine.
Humans Neoplasms Mutation Machine Learning Precision Medicine
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Intratumoral Resolution of Driver Gene Mutation Heterogeneity in Renal Cancer Using Deep Learning.

Cancer research Aug 3, 2022
UNLABELLED: Intratumoral heterogeneity arising from tumor evolution poses significant challenges biologically and clinically. Dissecting this complexity may benefit from deep learning (DL) algorithms, which can infer molecular features from ubiquitou...
Ubiquitin Thiolesterase Kidney Neoplasms Carcinoma, Renal Cell Mutation Humans Deep Learning Tumor Suppressor Proteins Animals Nuclear Proteins
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EPIMUTESTR: a nearest neighbor machine learning approach to predict cancer driver genes from the evolutionary action of coding variants.

Nucleic acids research Jul 8, 2022
Discovering rare cancer driver genes is difficult because their mutational frequency is too low for statistical detection by computational methods. EPIMUTESTR is an integrative nearest-neighbor machine learning algorithm that identifies such marginal...
Oncogenes Phylogeny Machine Learning Mutation Neoplasms Humans
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Artificial Intelligence-Assisted Serial Analysis of Clinical Cancer Genomics Data Identifies Changing Treatment Recommendations and Therapeutic Targets.

Clinical cancer research : an official journal of the American Association for Cancer Research Jun 1, 2022
PURPOSE: Given the pace of predictive biomarker and targeted therapy development, it is unknown whether repeat annotation of the same next-generation sequencing data can identify additional clinically actionable targets that could be therapeutically ...
Genomics Mutation Biomarkers, Tumor High-Throughput Nucleotide Sequencing Humans Neoplasms Artificial Intelligence Retrospective Studies
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SPLDExtraTrees: robust machine learning approach for predicting kinase inhibitor resistance.

Briefings in bioinformatics May 13, 2022
Drug resistance is a major threat to the global health and a significant concern throughout the clinical treatment of diseases and drug development. The mutation in proteins that is related to drug binding is a common cause for adaptive drug resistan...
Mutation Ligands Machine Learning Molecular Dynamics Simulation Proteins
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