Support vector machine approach for protein subcellular localization prediction

Clicks: 1
ID: 302198
2001
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #670 of 825 articles by views in BMC Bioinformatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 825 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Subcellular localization is a key functional characteristic of proteins. A fully automatic and reliable prediction system for protein subcellular localization is needed, especially for the analysis of large-scale genome sequences.In this paper, Support Vector Machine has been introduced to predict the subcellular localization of proteins from their amino acid compositions. The total prediction accuracies reach 91.4% for three subcellular locations in prokaryotic organisms and 79.4% for four locations in eukaryotic organisms. Predictions by our approach are robust to errors in the protein N-terminal sequences. This new approach provides superior prediction performance compared with existing algorithms based on amino acid composition and can be a complementary method to other existing methods based on sorting signals.A web server implementing the prediction method is available at http://www.bioinfo.tsinghua.edu.cn/SubLoc/.Supplementary material is available at http://www.bioinfo.tsinghua.edu.cn/SubLoc/.
Reference Key
openalex_W2160979370 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Sujun Hua, Zhirong Sun
Journal BMC Bioinformatics
Year 2001
DOI
10.1093/bioinformatics/17.8.721
URL
https://academic.oup.com/bioinformatics/article-pdf/17/8/721/48837192/bioinformatics_17_8_721.pdf
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.