AIMC Topic:
Supervised Machine Learning
Clear Filters Showing 1591 to 1600 of 1640 articles
Orchid: a novel management, annotation and machine learning framework for analyzing cancer mutations.
Bioinformatics (Oxford, England)
Mar 15, 2018
MOTIVATION: As whole-genome tumor sequence and biological annotation datasets grow in size, number and content, there is an increasing basic science and clinical need for efficient and accurate data management and analysis software. With the emergenc...
A probabilistic pathway score (PROPS) for classification with applications to inflammatory bowel disease.
Bioinformatics (Oxford, England)
Mar 15, 2018
SUMMARY: Gene-based supervised machine learning classification models have been widely used to differentiate disease states, predict disease progression and determine effective treatment options. However, many of these classifiers are sensitive to no...
Cross-Modality Image Synthesis via Weakly Coupled and Geometry Co-Regularized Joint Dictionary Learning.
IEEE transactions on medical imaging
Mar 1, 2018
Multi-modality medical imaging is increasingly used for comprehensive assessment of complex diseases in either diagnostic examinations or as part of medical research trials. Different imaging modalities provide complementary information about living ...
DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier.
Bioinformatics (Oxford, England)
Feb 15, 2018
MOTIVATION: A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often ...
Genome-wide pre-miRNA discovery from few labeled examples.
Bioinformatics (Oxford, England)
Feb 15, 2018
MOTIVATION: Although many machine learning techniques have been proposed for distinguishing miRNA hairpins from other stem-loop sequences, most of the current methods use supervised learning, which requires a very good set of positive and negative ex...
Supervised Machine Learning Algorithms for Evaluation of Solid Lipid Nanoparticles and Particle Size.
Combinatorial chemistry & high throughput screening
Jan 1, 2018
AIMS AND OBJECTIVES: Solid Lipid Nanoparticles (SLNs) are pharmaceutical delivery systems that have advantages such as controlled drug release, long-term stability etc. Particle Size (PS) is one of the important criteria of SLNs. These factors affect...
Automatic Annotation Tool to Support Supervised Machine Learning for Scaphoid Fracture Detection.
Studies in health technology and informatics
Jan 1, 2018
The aim of this work is to develop and validate an automatic annotation tool for the detection and bone localization of scaphoid fractures in radiology reports. To achieve this goal, a rule-based method using a Natural Language Processing (NLP) tool ...
Low dose CT reconstruction via L1 norm dictionary learning using alternating minimization algorithm and balancing principle.
Journal of X-ray science and technology
Jan 1, 2018
Excessive radiation exposure in computed tomography (CT) scans increases the chance of developing cancer and has become a major clinical concern. Recently, statistical iterative reconstruction (SIR) with l0-norm dictionary learning regularization has...
Diabetic macular edema grading in retinal images using vector quantization and semi-supervised learning.
Technology and health care : official journal of the European Society for Engineering and Medicine
Jan 1, 2018
BACKGROUND: Diabetic macular edema (DME) is one of the severe complication of diabetic retinopathy causing severe vision loss and leads to blindness in severe cases if left untreated.
Improving Layman Readability of Clinical Narratives with Unsupervised Synonym Replacement.
Studies in health technology and informatics
Jan 1, 2018
We report on the development and evaluation of a prototype tool aimed to assist laymen/patients in understanding the content of clinical narratives. The tool relies largely on unsupervised machine learning applied to two large corpora of unlabeled te...
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