AIMC Topic: Deep Learning

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Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study.

The Lancet. Oncology Jan 1, 2021
BACKGROUND: Detecting microsatellite instability (MSI) in colorectal cancer is crucial for clinical decision making, as it identifies patients with differential treatment response and prognosis. Universal MSI testing is recommended, but many patients...
Microscopy Image Interpretation, Computer-Assisted Eosine Yellowish-(YS) Diagnosis, Computer-Assisted Microsatellite Instability Deep Learning Coloring Agents Staining and Labeling Predictive Value of Tests Genetic Predisposition to Disease Humans Reproducibility of Results Phenotype Colorectal Neoplasms Hematoxylin
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Intelligent medical image feature extraction method based on improved deep learning.

Technology and health care : official journal of the European Society for Engineering and Medicine Jan 1, 2021
BACKGROUND: Medical patients can be diagnosed early, however it is difficult to extract effective features in medical image segmentation based on semantic information.
Deep Learning Image Processing, Computer-Assisted Neural Networks, Computer Humans Machine Learning Brain Neoplasms
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DeepSSPred: A Deep Learning Based Sulfenylation Site Predictor Via a Novel nSegmented Optimize Federated Feature Encoder.

Protein and peptide letters Jan 1, 2021
BACKGROUND: S-sulfenylation (S-sulphenylation, or sulfenic acid) proteins, are special kinds of post-translation modification, which plays an important role in various physiological and pathological processes such as cytokine signaling, transcription...
Protein Processing, Post-Translational Proteins Sulfenic Acids Databases, Protein Machine Learning Deep Learning Computational Biology
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Sequence-Based Deep Learning Frameworks on Enhancer-Promoter Interactions Prediction.

Current pharmaceutical design Jan 1, 2021
Enhancer-promoter interactions (EPIs) in the human genome are of great significance to transcriptional regulation, which tightly controls gene expression. Identification of EPIs can help us better decipher gene regulation and understand disease mecha...
Humans Genome, Human Promoter Regions, Genetic Neural Networks, Computer Regulatory Sequences, Nucleic Acid Deep Learning
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Deep Transfer Learning for COVID-19 Prediction: Case Study for Limited Data Problems.

Current medical imaging Jan 1, 2021
OBJECTIVE: Automatic prediction of COVID-19 using deep convolution neural networks based pre-trained transfer models and Chest X-ray images.
Radiography, Thoracic Humans Neural Networks, Computer SARS-CoV-2 Deep Learning COVID-19
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Denoising Medical Images Using Machine Learning, Deep Learning Approaches: A Survey.

Current medical imaging Jan 1, 2021
OBJECTIVE: Several denoising methods for medical images have been applied, such as Wavelet Transform, CNN, linear and Non-linear methods.
Normal Distribution Algorithms Deep Learning Signal-To-Noise Ratio Neural Networks, Computer
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Model assessment: new measures should be known and traditional measures should be accurately interpreted.

European heart journal Jan 1, 2021
Humans Coronary Artery Disease Feasibility Studies Deep Learning
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Time for clinicians to revisit their perspectives on C-statistic.

European heart journal Jan 1, 2021
Emergency Service, Hospital Coronary Artery Disease Feasibility Studies Humans Deep Learning Patient Readmission
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Using artificial intelligence to assist radiologists in distinguishing COVID-19 from other pulmonary infections.

Journal of X-ray science and technology Jan 1, 2021
BACKGROUND: Accurate and rapid diagnosis of coronavirus disease (COVID-19) is crucial for timely quarantine and treatment.
Artificial Intelligence COVID-19 Algorithms Humans Diagnosis, Differential Radiologists Respiratory Tract Infections Deep Learning Clinical Competence Adult Sensitivity and Specificity Middle Aged Lung Male SARS-CoV-2 Aged Young Adult Tomography, X-Ray Computed Female
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Variable Length Character N-Gram Embedding of Protein Sequences for Secondary Structure Prediction.

Protein and peptide letters Jan 1, 2021
BACKGROUND: The prediction of a protein's secondary structure from its amino acid sequence is an essential step towards predicting its 3-D structure. The prediction performance improves by incorporating homologous multiple sequence alignment informat...
Databases, Protein Protein Structure, Secondary Sequence Analysis, Protein Deep Learning Proteins
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