AIMC Topic: DNA Mutational Analysis
Clear Filters Showing 11 to 20 of 34 articles
Diagnostic accuracy and potential covariates for machine learning to identify IDH mutations in glioma patients: evidence from a meta-analysis.
European radiology
Mar 19, 2020
OBJECTIVES: To assess the diagnostic accuracy of machine learning (ML) in predicting isocitrate dehydrogenase (IDH) mutations in patients with glioma and to identify potential covariates that could influence the diagnostic performance of ML.
Virtual genetic diagnosis for familial hypercholesterolemia powered by machine learning.
European journal of preventive cardiology
Feb 4, 2020
AIMS: Familial hypercholesterolemia (FH) is the most common genetic disorder of lipid metabolism. The gold standard for FH diagnosis is genetic testing, available, however, only in selected university hospitals. Clinical scores - for example, the Dut...
Novel approaches for glioblastoma treatment: Focus on tumor heterogeneity, treatment resistance, and computational tools.
Cancer reports (Hoboken, N.J.)
Nov 11, 2019
BACKGROUND: Glioblastoma (GBM) is a highly aggressive primary brain tumor. Currently, the suggested line of action is the surgical resection followed by radiotherapy and treatment with the adjuvant temozolomide, a DNA alkylating agent. However, the a...
Drug Resistance, Neoplasm
Microtubules
Tumor Suppressor Protein p53
Tumor Suppressor Proteins
Clonal Evolution
DNA Modification Methylases
DNA Mutational Analysis
DNA Repair Enzymes
Prognosis
Tumor Microenvironment
Brain Neoplasms
Immune Checkpoint Inhibitors
Computational Biology
Humans
Cancer Vaccines
Machine Learning
Models, Genetic
Brain
Antineoplastic Combined Chemotherapy Protocols
Precision Medicine
Glioblastoma
Mutation
Genetic Heterogeneity
Chemoradiotherapy, Adjuvant
Detection of the BRAF V600E Mutation in Colorectal Cancer by NIR Spectroscopy in Conjunction with Counter Propagation Artificial Neural Network.
Molecules (Basel, Switzerland)
Jun 15, 2019
This paper proposes a sensitive, sample preparation-free, rapid, and low-cost method for the detection of the B-rapidly accelerated fibrosarcoma (BRAF) gene mutation involving a substitution of valine to glutamic acid at codon 600 (V600E) in colorect...
Deep convolutional neural networks for accurate somatic mutation detection.
Nature communications
Mar 4, 2019
Accurate detection of somatic mutations is still a challenge in cancer analysis. Here we present NeuSomatic, the first convolutional neural network approach for somatic mutation detection, which significantly outperforms previous methods on different...
A multi-task convolutional deep neural network for variant calling in single molecule sequencing.
Nature communications
Mar 1, 2019
The accurate identification of DNA sequence variants is an important, but challenging task in genomics. It is particularly difficult for single molecule sequencing, which has a per-nucleotide error rate of ~5-15%. Meeting this demand, we developed Cl...
A hybrid approach for automated mutation annotation of the extended human mutation landscape in scientific literature.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Dec 5, 2018
As the cost of DNA sequencing continues to fall, an increasing amount of information on human genetic variation is being produced that could help progress precision medicine. However, information about such mutations is typically first made available...
A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data.
Nature genetics
Nov 5, 2018
Cancer genomic analysis requires accurate identification of somatic variants in sequencing data. Manual review to refine somatic variant calls is required as a final step after automated processing. However, manual variant refinement is time-consumin...
A universal SNP and small-indel variant caller using deep neural networks.
Nature biotechnology
Sep 24, 2018
Despite rapid advances in sequencing technologies, accurately calling genetic variants present in an individual genome from billions of short, errorful sequence reads remains challenging. Here we show that a deep convolutional neural network can call...
A Cascaded Deep Convolutional Neural Network for Joint Segmentation and Genotype Prediction of Brainstem Gliomas.
IEEE transactions on bio-medical engineering
Jun 8, 2018
GOAL: Automatic segmentation of brainstem gliomas and prediction of genotype (H3 K27M) mutation status based on magnetic resonance (MR) images are crucial but challenging tasks for computer-aided diagnosis in neurosurgery. In this paper, we present a...
Don't Miss the Future of Medicine
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 30978
- Neural Networks, Computer 29696
- Algorithms 26813
- Deep Learning 26324
- Artificial Intelligence 24267
- Robotics 13307
- Image Processing, Computer-Assisted 9512
- Retrospective Studies 9097
- Magnetic Resonance Imaging 6173
- Reproducibility of Results 5589
- Tomography, X-Ray Computed 4765
- Support Vector Machine 4678
- Computational Biology 4229
- Brain 4045
- Computer Simulation 3877
Recent Journals
- Scientific reports 5865
- Sensors (Basel, Switzerland) 4794
- PloS one 4011
- Neural networks : the official journal of the International Neural Network Society 2928
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1839
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1278
- IEEE journal of biomedical and health informatics 1129
- Journal of chemical information and modeling 994
- Nature communications 938
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 861
- Briefings in bioinformatics 850
- IEEE transactions on neural networks and learning systems 823