AIMC Topic:
Supervised Machine Learning
Clear Filters Showing 1611 to 1620 of 1640 articles
Active learning for bird sound classification via a kernel-based extreme learning machine.
The Journal of the Acoustical Society of America
Oct 1, 2017
In recent years, research fields, including ecology, bioacoustics, signal processing, and machine learning, have made bird sound recognition a part of their focus. This has led to significant advancements within the field of ornithology, such as impr...
An efficient graph kernel method for non-coding RNA functional prediction.
Bioinformatics (Oxford, England)
Sep 1, 2017
MOTIVATION: The importance of RNA protein-coding gene regulation is by now well appreciated. Non-coding RNAs (ncRNAs) are known to regulate gene expression at practically every stage, ranging from chromatin packaging to mRNA translation. However the ...
BIOSSES: a semantic sentence similarity estimation system for the biomedical domain.
Bioinformatics (Oxford, England)
Jul 15, 2017
MOTIVATION: The amount of information available in textual format is rapidly increasing in the biomedical domain. Therefore, natural language processing (NLP) applications are becoming increasingly important to facilitate the retrieval and analysis o...
Unity is Strength: Improving biomedical classification performance based on ensemble learning approaches.
Computer methods and programs in biomedicine
Apr 1, 2017
Application of supervised machine learning algorithms for the classification of regulatory RNA riboswitches.
Briefings in functional genomics
Mar 1, 2017
Riboswitches, the small structured RNA elements, were discovered about a decade ago. It has been the subject of intense interest to identify riboswitches, understand their mechanisms of action and use them in genetic engineering. The accumulation of ...
Optimizing ChIP-seq peak detectors using visual labels and supervised machine learning.
Bioinformatics (Oxford, England)
Feb 15, 2017
MOTIVATION: Many peak detection algorithms have been proposed for ChIP-seq data analysis, but it is not obvious which algorithm and what parameters are optimal for any given dataset. In contrast, regions with and without obvious peaks can be easily l...
Comparison of machine-learning algorithms to build a predictive model for detecting undiagnosed diabetes - ELSA-Brasil: accuracy study.
Sao Paulo medical journal = Revista paulista de medicina
Jan 1, 2017
CONTEXT AND OBJECTIVE:: Type 2 diabetes is a chronic disease associated with a wide range of serious health complications that have a major impact on overall health. The aims here were to develop and validate predictive models for detecting undiagnos...
Supervised Machine Learning Methods Applied to Predict Ligand- Binding Affinity.
Current medicinal chemistry
Jan 1, 2017
BACKGROUND: Calculation of ligand-binding affinity is an open problem in computational medicinal chemistry. The ability to computationally predict affinities has a beneficial impact in the early stages of drug development, since it allows a mathemati...
An Efficient Semi-supervised Learning Approach to Predict SH2 Domain Mediated Interactions.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2017
Src homology 2 (SH2) domain is an important subclass of modular protein domains that plays an indispensable role in several biological processes in eukaryotes. SH2 domains specifically bind to the phosphotyrosine residue of their binding peptides to ...
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