AIMC Topic: Machine Learning

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Biomedical informatics and machine learning for clinical genomics.

Human molecular genetics May 1, 2018
While tens of thousands of pathogenic variants are used to inform the many clinical applications of genomics, there remains limited information on quantitative disease risk for the majority of variants used in clinical practice. At the same time, ris...
Humans Machine Learning Genomics Computational Biology Databases, Genetic Genome, Human
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Deep learning applications in ophthalmology.

Current opinion in ophthalmology May 1, 2018
PURPOSE OF REVIEW: To describe the emerging applications of deep learning in ophthalmology.
Macular Degeneration Diagnostic Techniques, Ophthalmological Photography Posterior Eye Segment Glaucoma Machine Learning Tomography, Optical Coherence Humans Diabetic Retinopathy
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A Preventive Model for Muscle Injuries: A Novel Approach based on Learning Algorithms.

Medicine and science in sports and exercise May 1, 2018
INTRODUCTION: The application of contemporary statistical approaches coming from Machine Learning and Data Mining environments to build more robust predictive models to identify athletes at high risk for injury might support injury prevention strateg...
Risk Factors Male ROC Curve Algorithms Prospective Studies Muscle, Skeletal Athletes Athletic Injuries Models, Theoretical Lower Extremity Decision Making Humans Machine Learning
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Computational identification of binding energy hot spots in protein-RNA complexes using an ensemble approach.

Bioinformatics (Oxford, England) May 1, 2018
MOTIVATION: Identifying RNA-binding residues, especially energetically favored hot spots, can provide valuable clues for understanding the mechanisms and functional importance of protein-RNA interactions. Yet, limited availability of experimentally r...
Models, Molecular RNA Binding Sites Computational Biology Proteins Machine Learning Databases, Protein Protein Binding
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DNCON2: improved protein contact prediction using two-level deep convolutional neural networks.

Bioinformatics (Oxford, England) May 1, 2018
MOTIVATION: Significant improvements in the prediction of protein residue-residue contacts are observed in the recent years. These contacts, predicted using a variety of coevolution-based and machine learning methods, are the key contributors to the ...
Sequence Analysis, Protein Caspases Protein Conformation Neural Networks, Computer Machine Learning Computational Biology
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Using machine learning for sequence-level automated MRI protocol selection in neuroradiology.

Journal of the American Medical Informatics Association : JAMIA May 1, 2018
Incorrect imaging protocol selection can lead to important clinical findings being missed, contributing to both wasted health care resources and patient harm. We present a machine learning method for analyzing the unstructured text of clinical indica...
Humans Machine Learning Magnetic Resonance Imaging Datasets as Topic Neuroradiography Sensitivity and Specificity Decision Support Systems, Clinical Support Vector Machine
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Machine Learning Compared With Pathologist Assessment-Reply.

JAMA Apr 24, 2018
Breast Neoplasms Humans Risk Assessment Machine Learning Pathologists
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Machine Learning Compared With Pathologist Assessment.

JAMA Apr 24, 2018
Humans Algorithms Machine Learning Breast Neoplasms Pathologists
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Prediction of Abnormal Myocardial Relaxation From Signal Processed Surface ECG.

Journal of the American College of Cardiology Apr 17, 2018
BACKGROUND: Myocardial relaxation is impaired in almost all cases with left ventricular diastolic dysfunction (LVDD) and is a strong predictor of cardiovascular and all-cause mortality.
Diastole Prospective Studies Female Aged Humans Machine Learning Electrocardiography Cross-Sectional Studies Heart Male Ventricular Dysfunction, Left Middle Aged
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Modeling positional effects of regulatory sequences with spline transformations increases prediction accuracy of deep neural networks.

Bioinformatics (Oxford, England) Apr 15, 2018
MOTIVATION: Regulatory sequences are not solely defined by their nucleic acid sequence but also by their relative distances to genomic landmarks such as transcription start site, exon boundaries or polyadenylation site. Deep learning has become the a...
Protein Binding Genomics Sequence Analysis, RNA RNA Models, Genetic Proteins K562 Cells Neural Networks, Computer DNA Sequence Analysis, DNA Regulatory Sequences, Nucleic Acid Machine Learning Humans Hep G2 Cells Software
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