ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2009
Support vector machine-based mucin-type o-linked glycosylation site prediction using enhanced sequence feature encoding.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 8 citations in OpenAlex.
- KSIMC: Predicting Kinase⁻Substrate Interactions Based on Matrix Completion.International journal of molecular sciences · 2019Article
- PKIS: computational identification of protein kinases for experimentally discovered protein phosphorylation sites.BMC bioinformatics · 2013Article
- dbOGAP - an integrated bioinformatics resource for protein O-GlcNAcylation.BMC bioinformatics · 2011Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Glycosylation is a common and complex protein post-translational modification (PTM). In particular, mucin-type O-linked glycosylation is abundant and plays important biological functions. The number of determined glycosylation sites is still small and there remains the need of accurate computational prediction for annotation and functional understanding of proteins. PTM site prediction can be formulated as a machine learning task. An important step in applying machine learning to this task is encoding protein fragments as feature vectors. Here we assess existing encoding methods as well as an enhanced encoding method named composition of monomer spectrum (CMS) using support vector machines (SVMs). SVMs employing the existing encoding methods achieved AUC (area under ROC curve) of 90.3-91.3%, and ones employing CMS achieved AUC of 92.4%. Analysis of different encoding methods suggests the potential in further improving the prediction.
Indexed as
Identifiers
20351933PMC2815398W50100400What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.