Multi-label multi-Kernel transfer learning for human protein subcellular localization

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Abstract

Recent years have witnessed much progress in computational modelling for protein subcellular localization. However, the existing sequence-based predictive models demonstrate moderate or unsatisfactory performance, and the gene ontology (GO) based models may take the risk of performance overestimation for novel proteins. Furthermore, many human proteins have multiple subcellular locations, which renders the computational modelling more complicated. Up to the present, there are far few researches specialized for predicting the subcellular localization of human proteins that may reside in multiple cellular compartments. In this paper, we propose a multi-label multi-kernel transfer learning model for human protein subcellular localization (MLMK-TLM). MLMK-TLM proposes a multi-label confusion matrix, formally formulates three multi-labelling performance measures and adapts one-against-all multi-class probabilistic outputs to multi-label learning scenario, based on which to further extends our published work GO-TLM (gene ontology based transfer learning model for protein subcellular localization) and MK-TLM (multi-kernel transfer learning based on Chou's PseAAC formulation for protein submitochondria localization) for multiplex human protein subcellular localization. With the advantages of proper homolog knowledge transfer, comprehensive survey of model performance for novel protein and multi-labelling capability, MLMK-TLM will gain more practical applicability. The experiments on human protein benchmark dataset show that MLMK-TLM significantly outperforms the baseline model and demonstrates good multi-labelling ability for novel human proteins. Some findings (predictions) are validated by the latest Swiss-Prot database. The software can be freely downloaded at http://soft.synu.edu.cn/upload/msy.rar. © 2012 Suyu Mei.

Figures

  • Figure 1. Illustration of divergent homolog in terms of subcellular localization. doi:10.1371/journal.pone.0037716.g001
  • Table 1. Optimal performance on 3681 human locative protein dataset.
  • Figure 2. Performance on 3681 human protein dataset with varying homologs. doi:10.1371/journal.pone.0037716.g002
  • Figure 3. Kernel weight estimation on 3681 human locative protein dataset. doi:10.1371/journal.pone.0037716.g003
  • Table 2. Multi-labelling evaluation for optimistic case.
  • Table 3. Multi-labelling evaluation for moderate case.
  • Table 4. Multi-labelling evaluation for pessimistic case.
  • Table 5. Multi-labelling evaluation—perfect label match.

References Powered by Scopus

Gapped BLAST and PSI-BLAST: A new generation of protein database search programs

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A survey on transfer learning

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The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003

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CITATION STYLE

APA

Mei, S. (2012). Multi-label multi-Kernel transfer learning for human protein subcellular localization. PLoS ONE, 7(6). https://doi.org/10.1371/journal.pone.0037716

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