AIMC Topic: Genome

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Genome-wide pre-miRNA discovery from few labeled examples.

Bioinformatics (Oxford, England)
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...

Complementary Sources of Protein Functional Information: The Far Side of GO.

Methods in molecular biology (Clifton, N.J.)
The GO captures many aspects of functional annotations, but there are other alternative complementary sources of protein function information. For example, enzyme functional annotations are described in a range of resources from the Enzyme Commission...

Evaluating Computational Gene Ontology Annotations.

Methods in molecular biology (Clifton, N.J.)
Two avenues to understanding gene function are complementary and often overlapping: experimental work and computational prediction. While experimental annotation generally produces high-quality annotations, it is low throughput. Conversely, computati...

Higher order methylation features for clustering and prediction in epigenomic studies.

Bioinformatics (Oxford, England)
MOTIVATION: DNA methylation is an intensely studied epigenetic mark, yet its functional role is incompletely understood. Attempts to quantitatively associate average DNA methylation to gene expression yield poor correlations outside of the well-under...

A Machine Learning Approach for Accurate Annotation of Noncoding RNAs.

IEEE/ACM transactions on computational biology and bioinformatics
Searching genomes to locate noncoding RNA genes with known secondary structure is an important problem in bioinformatics. In general, the secondary structure of a searched noncoding RNA is defined with a structure model constructed from the structura...