AIMC Topic:
Supervised Machine Learning

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Distributed semi-supervised support vector machines.

Neural networks : the official journal of the International Neural Network Society Apr 27, 2016
The semi-supervised support vector machine (S(3)VM) is a well-known algorithm for performing semi-supervised inference under the large margin principle. In this paper, we are interested in the problem of training a S(3)VM when the labeled and unlabel...
Supervised Machine Learning Algorithms Support Vector Machine
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Semi-supervised clustering of fractionated electrograms for electroanatomical atrial mapping.

Biomedical engineering online Apr 26, 2016
BACKGROUND: Electrogram-guided ablation procedures have been proposed as an alternative strategy consisting of either mapping and ablating focal sources or targeting complex fractionated electrograms in atrial fibrillation (AF). However, the incomple...
Cluster Analysis Signal Processing, Computer-Assisted Supervised Machine Learning Humans Databases, Factual Electrophysiologic Techniques, Cardiac Heart Atria Nonlinear Dynamics
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Incorporating organelle correlations into semi-supervised learning for protein subcellular localization prediction.

Bioinformatics (Oxford, England) Apr 23, 2016
MOTIVATION: Bioimages of subcellular protein distribution as a new data source have attracted much attention in the field of automated prediction of proteins subcellular localization. Performance of existing systems is significantly limited by the sm...
Protein Transport Fluorescent Antibody Technique Organelles Supervised Machine Learning Immunohistochemistry Humans Computational Biology Bayes Theorem Algorithms
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Respiratory Artefact Removal in Forced Oscillation Measurements: A Machine Learning Approach.

IEEE transactions on bio-medical engineering Apr 15, 2016
GOAL: Respiratory artefact removal for the forced oscillation technique can be treated as an anomaly detection problem. Manual removal is currently considered the gold standard, but this approach is laborious and subjective. Most existing automated t...
Pattern Recognition, Automated Adult Airway Resistance Supervised Machine Learning Respiratory Function Tests Sensitivity and Specificity Oscillometry Female Humans Artifacts Respiratory Mechanics Diagnosis, Computer-Assisted Reproducibility of Results Male Algorithms Child
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Deciphering the Complexity of Ligand-Protein Recognition Pathways Using Supervised Molecular Dynamics (SuMD) Simulations.

Journal of chemical information and modeling Apr 14, 2016
Molecular recognition is a crucial issue when aiming to interpret the mechanism of known active substances as well as to develop novel active candidates. Unfortunately, simulating the binding process is still a challenging task because it requires cl...
Molecular Dynamics Simulation Supervised Machine Learning Protein Binding Cell Membrane Ligands Receptors, G-Protein-Coupled Protein Conformation
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Classifying Force Spectroscopy of DNA Pulling Measurements Using Supervised and Unsupervised Machine Learning Methods.

Journal of chemical information and modeling Apr 4, 2016
Dynamic force spectroscopy (DFS) measurements on biomolecules typically require classifying thousands of repeated force spectra prior to data analysis. Here, we study classification of atomic force microscope-based DFS measurements using machine-lear...
DNA Supervised Machine Learning Ultraviolet Rays Biomechanical Phenomena Unsupervised Machine Learning Base Sequence Statistics as Topic Azo Compounds Mechanical Phenomena Spectrum Analysis
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Segmenting Retinal Blood Vessels With Deep Neural Networks.

IEEE transactions on medical imaging Mar 24, 2016
The condition of the vascular network of human eye is an important diagnostic factor in ophthalmology. Its segmentation in fundus imaging is a nontrivial task due to variable size of vessels, relatively low contrast, and potential presence of patholo...
Humans Retinal Vessels Neural Networks, Computer Supervised Machine Learning Databases, Factual Image Interpretation, Computer-Assisted
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Supervised domain adaptation of decision forests: Transfer of models trained in vitro for in vivo intravascular ultrasound tissue characterization.

Medical image analysis Mar 17, 2016
In this paper, we propose a supervised domain adaptation (DA) framework for adapting decision forests in the presence of distribution shift between training (source) and testing (target) domains, given few labeled examples. We introduce a novel metho...
Sensitivity and Specificity Ultrasonography Heart Supervised Machine Learning Image Processing, Computer-Assisted Humans Reproducibility of Results Coronary Circulation
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Super-Thresholding: Supervised Thresholding of Protein Crystal Images.

IEEE/ACM transactions on computational biology and bioinformatics Mar 16, 2016
In general, a single thresholding technique is developed or enhanced to separate foreground objects from background for a domain of images. This idea may not generate satisfactory results for all images in a dataset, since different images may requir...
Supervised Machine Learning Crystallization Image Processing, Computer-Assisted Proteins Algorithms Databases, Protein
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GOexpress: an R/Bioconductor package for the identification and visualisation of robust gene ontology signatures through supervised learning of gene expression data.

BMC bioinformatics Mar 11, 2016
BACKGROUND: Identification of gene expression profiles that differentiate experimental groups is critical for discovery and analysis of key molecular pathways and also for selection of robust diagnostic or prognostic biomarkers. While integration of ...
Gene Ontology RNA, Messenger Supervised Machine Learning Transcriptome Computational Biology Software
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