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

Overview of attention for article published in BMC Systems Biology, January 2015
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Title
An empirical study of ensemble-based semi-supervised learning approaches for imbalanced splice site datasets
Published in
BMC Systems Biology, January 2015
DOI 10.1186/1752-0509-9-s5-s1
Pubmed ID
Authors

Ana Stanescu, Doina Caragea

Abstract

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 process of labeling data can be expensive, as it requires domain knowledge and expert involvement. Semi-supervised learning approaches that can make use of unlabeled data, in addition to small amounts of labeled data, can help reduce the costs associated with labeling. In this context, we focus on the problem of predicting splice sites in a genome using semi-supervised learning approaches. This is a challenging problem, due to the highly imbalanced distribution of the data, i.e., small number of splice sites as compared to the number of non-splice sites. To address this challenge, we propose to use ensembles of semi-supervised classifiers, specifically self-training and co-training classifiers. Our experiments on five highly imbalanced splice site datasets, with positive to negative ratios of 1-to-99, showed that the ensemble-based semi-supervised approaches represent a good choice, even when the amount of labeled data consists of less than 1% of all training data. In particular, we found that ensembles of co-training and self-training classifiers that dynamically balance the set of labeled instances during the semi-supervised iterations show improvements over the corresponding supervised ensemble baselines. In the presence of limited amounts of labeled data, ensemble-based semi-supervised approaches can successfully leverage the unlabeled data to enhance supervised ensembles learned from highly imbalanced data distributions. Given that such distributions are common for many biological sequence classification problems, our work can be seen as a stepping stone towards more sophisticated ensemble-based approaches to biological sequence annotation in a semi-supervised framework.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 6%
Unknown 16 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 24%
Student > Bachelor 3 18%
Student > Doctoral Student 3 18%
Other 1 6%
Lecturer 1 6%
Other 2 12%
Unknown 3 18%
Readers by discipline Count As %
Computer Science 7 41%
Engineering 2 12%
Biochemistry, Genetics and Molecular Biology 1 6%
Medicine and Dentistry 1 6%
Pharmacology, Toxicology and Pharmaceutical Science 1 6%
Other 0 0%
Unknown 5 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 January 2016.
All research outputs
#5,194,734
of 6,956,879 outputs
Outputs from BMC Systems Biology
#576
of 792 outputs
Outputs of similar age
#208,865
of 305,057 outputs
Outputs of similar age from BMC Systems Biology
#26
of 32 outputs
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