Human Protein Subcellular Localization with Integrated Source and Multi-label Ensemble Classifier

Predicting protein subcellular location is necessary for understanding cell function. Several machine learning methods have been developed for computational prediction of primary protein sequences because wet experiments are costly and time consuming. However, two problems still exist in state-of-the-art methods. First, several proteins appear in different subcellular structures simultaneously, whereas current methods only predict one protein sequence in one subcellular structure. Second, most software tools are trained with obsolete data and the latest new databases are missed. We proposed a novel multi-label classification algorithm to solve the first problem and integrated several latest databases to improve prediction performance. Experiments proved the effectiveness of the proposed method. The present study would facilitate research on cellular proteomics.

• Publication year

  2016

• Venue

  Scientific Reports

• Publication date

  2016-06-21

• Fields of study

  Biology, Medicine, Computer Science

• Identifiers
  DOI 10.1038/srep28087PMID 27323846PMCID 4914962
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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