AIMC Topic:
Supervised Machine Learning

Clear Filters Showing 1361 to 1370 of 1635 articles

Sparse Markov chain-based semi-supervised multi-instance multi-label method for protein function prediction.

Journal of bioinformatics and computational biology Sep 16, 2015
Automated assignment of protein function has received considerable attention in recent years for genome-wide study. With the rapid accumulation of genome sequencing data produced by high-throughput experimental techniques, the process of manually pre...
Algorithms Animals Genome-Wide Association Study Databases, Protein Supervised Machine Learning Computational Biology Markov Chains Proteins
View on PubMed DOI

An empirical study of ensemble-based semi-supervised learning approaches for imbalanced splice site datasets.

BMC systems biology Sep 1, 2015
BACKGROUND: Recent biochemical advances have led to inexpensive, time-efficient production of massive volumes of raw genomic data. Traditional machine learning approaches to genome annotation typically rely on large amounts of labeled data. The proce...
Molecular Sequence Annotation Databases, Genetic Algorithms RNA Splicing Genomics Supervised Machine Learning
View on PubMed DOI

Dynamic identifying protein functional modules based on adaptive density modularity in protein-protein interaction networks.

BMC bioinformatics Aug 25, 2015
BACKGROUND: The identification of protein functional modules would be a great aid in furthering our knowledge of the principles of cellular organization. Most existing algorithms for identifying protein functional modules have a common defect -- once...
Saccharomyces cerevisiae Proteins Saccharomyces cerevisiae Protein Interaction Maps Algorithms Gene Ontology Supervised Machine Learning
View on PubMed DOI

Protein complex detection in PPI networks based on data integration and supervised learning method.

BMC bioinformatics Aug 25, 2015
BACKGROUND: Revealing protein complexes are important for understanding principles of cellular organization and function. High-throughput experimental techniques have produced a large amount of protein interactions, which makes it possible to predict...
Protein Interaction Maps Saccharomyces cerevisiae Proteins Supervised Machine Learning Saccharomyces cerevisiae Gene Ontology
View on PubMed DOI

Using distant supervised learning to identify protein subcellular localizations from full-text scientific articles.

Journal of biomedical informatics Jul 26, 2015
Databases of curated biomedical knowledge, such as the protein-locations reflected in the UniProtKB database, provide an accurate and useful resource to researchers and decision makers. Our goal is to augment the manual efforts currently used to cura...
Databases, Protein Proteins Knowledge Bases Supervised Machine Learning Data Curation
View on PubMed DOI

Use of Semisupervised Clustering and Feature-Selection Techniques for Identification of Co-expressed Genes.

IEEE journal of biomedical and health informatics Jul 20, 2015
Studying the patterns hidden in gene-expression data helps to understand the functionality of genes. In general, clustering techniques are widely used for the identification of natural partitionings from the gene expression data. In order to put cons...
Humans Algorithms Computational Biology Rats Databases, Genetic Animals Gene Expression Profiling Supervised Machine Learning Yeasts Fuzzy Logic Arabidopsis Cluster Analysis
View on PubMed DOI

Maximum margin semi-supervised learning with irrelevant data.

Neural networks : the official journal of the International Neural Network Society Jul 15, 2015
Semi-supervised learning (SSL) is a typical learning paradigms training a model from both labeled and unlabeled data. The traditional SSL models usually assume unlabeled data are relevant to the labeled data, i.e., following the same distributions of...
Artificial Intelligence Algorithms Support Vector Machine Models, Neurological Image Processing, Computer-Assisted Supervised Machine Learning Models, Statistical Entropy Data Interpretation, Statistical
View on PubMed DOI

Semi-supervised Learning for the BioNLP Gene Regulation Network.

BMC bioinformatics Jul 13, 2015
BACKGROUND: The BioNLP Gene Regulation Task has attracted a diverse collection of submissions showcasing state-of-the-art systems. However, a principal challenge remains in obtaining a significant amount of recall. We argue that this is an important ...
Humans Gene Regulatory Networks Data Mining Computational Biology Information Storage and Retrieval Genes Supervised Machine Learning
View on PubMed DOI

Weakly Supervised Human Fixations Prediction.

IEEE transactions on cybernetics Jul 8, 2015
Automatically predicting human eye fixations is a useful technique that can facilitate many multimedia applications, e.g., image retrieval, action recognition, and photo retargeting. Conventional approaches are frustrated by two drawbacks. First, psy...
Fixation, Ocular Humans Psychophysics Algorithms Models, Statistical Image Processing, Computer-Assisted Supervised Machine Learning
View on PubMed DOI

Weighting training images by maximizing distribution similarity for supervised segmentation across scanners.

Medical image analysis Jul 7, 2015
Many automatic segmentation methods are based on supervised machine learning. Such methods have proven to perform well, on the condition that they are trained on a sufficiently large manually labeled training set that is representative of the images ...
Diffusion Magnetic Resonance Imaging Subtraction Technique Statistical Distributions Data Interpretation, Statistical Supervised Machine Learning Pattern Recognition, Automated Image Enhancement Image Interpretation, Computer-Assisted Humans Reproducibility of Results Sensitivity and Specificity Algorithms Brain
View on PubMed DOI
Popular Topics
Recent Journals