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An empirical study of ensemble-based semi-supervised learning approaches for imbalanced splice site datasets
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 process of labeling data can be expensive, as it requir...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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BioMed Central
2015
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4565116/ https://www.ncbi.nlm.nih.gov/pubmed/26356316 http://dx.doi.org/10.1186/1752-0509-9-S5-S1 |