Name
Title | ||
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SubLocEP: a novel ensemble predictor of subcellular localization of eukaryotic mRNA based on machine learning |
Abstract | ||
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Motivation: mRNA location corresponds to the location of protein translation and contributes to precise spatial and temporal management of the protein function. However, current assignment of subcellular localization of eukaryotic mRNA reveals important limitations: (1) turning multiple classifications into multiple dichotomies makes the training process tedious; (2) the majority of the models trained by classical algorithm are based on the extraction of single sequence information; (3) the existing state-of-the-art models have not reached an ideal level in terms of prediction and generalization ability. To achieve better assignment of subcellular localization of eukaryotic mRNA, a better and more comprehensive model must be developed. Results: In this paper, SubLocEP is proposed as a two-layer integrated prediction model for accurate prediction of the location of sequence samples. Unlike the existing models based on limited features, SubLocEP comprehensively considers additional feature attributes and is combined with LightGBM to generated single feature classifiers. The initial integration model (single-layer model) is generated according to the categories of a feature. Subsequently, two single-layer integration models are weighted (sequence-based: physicochemical properties = 3:2) to produce the final two-layer model. The performance of SubLocEP on independent datasets is sufficient to indicate that SubLocEP is an accurate and stable prediction model with strong generalization ability. Additionally, an online tool has been developed that contains experimental data and can maximize the user convenience for estimation of subcellular localization of eukaryotic mRNA. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1093/bib/bbaa401 | BRIEFINGS IN BIOINFORMATICS |
Keywords | DocType | Volume |
subcellular localization of eukaryotic mRNA, feature extraction, ensemble model, LightGBM | Journal | 22 |
Issue | ISSN | Citations |
5 | 1467-5463 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |