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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Imangaliyev, S. Keijser, B. Crielaard, W. Tsivtsivadze, E. |
| Copyright Year | 2013 |
| Description | Author affiliation: Top Inst. Food & Nutrition, Wageningen, Netherlands (Imangaliyev, S.; Keijser, B.; Crielaard, W.; Tsivtsivadze, E.) |
| Abstract | As the amount of metagenomic data grows rapidly, online statistical learning algorithms are poised to play key role in metagenome analysis tasks. Frequently, data are only partially labeled, namely dataset contains partial information about the problem of interest. This work presents an algorithm and a learning framework that is naturally suitable for the analysis of large scale, partially labeled metagenome datasets. We propose an online multi-output algorithm that learns by sequentially co-regularizing prediction functions on unlabeled data points and provides improved performance in comparison to several supervised methods. We evaluate predictive performance of the proposed methods on NIH Human Microbiome Project dataset. In particular we address the task of predicting relative abundance of Porphyromonas species in the oral cavity. In our empirical evaluation the proposed method outperforms several supervised regression techniques as well as leads to notable computational benefits when training the predictive model. |
| Sponsorship | IEEE Comput.Soc. |
| Starting Page | 521 |
| Ending Page | 525 |
| File Size | 151257 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781479913091 |
| DOI | 10.1109/BIBM.2013.6732550 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-12-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training Support vector machines Hidden Markov models Predictive models Prediction algorithms Data models Diseases |
| Content Type | Text |
| Resource Type | Article |
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