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Multi-label multi-instance transfer learning for simultaneous reconstruction and cross-talk modeling of multiple human signaling pathways.

Mei S, Zhu H - BMC Bioinformatics (2015)

Bottom Line: The predicted signaling components are further linked to pathways using the experimentally derived PPIs (protein-protein interactions) to reconstruct the human signaling pathways.Multi-label learning framework has been demonstrated effective in this work to model the phenomena that a signaling protein belongs to more than one signaling pathway.As results, novel signaling components and pathways targeted proteins are predicted to simultaneously reconstruct multiple human signaling pathways and the static map of their cross-talks for further biomedical research.

View Article: PubMed Central - PubMed

Affiliation: Software College, Shenyang Normal University, Shenyang, China. meisygle@gmail.com.

ABSTRACT

Background: Signaling pathways play important roles in the life processes of cell growth, cell apoptosis and organism development. At present the signal transduction networks are far from complete. As an effective complement to experimental methods, computational modeling is suited to rapidly reconstruct the signaling pathways at low cost. To our knowledge, the existing computational methods seldom simultaneously exploit more than three signaling pathways into one predictive model for the discovery of novel signaling components and the cross-talk modeling between signaling pathways.

Results: In this work, we propose a multi-label multi-instance transfer learning method to simultaneously reconstruct 27 human signaling pathways and model their cross-talks. Computational results show that the proposed method demonstrates satisfactory multi-label learning performance and rational proteome-wide predictions. Some predicted signaling components or pathway targeted proteins have been validated by recent literature. The predicted signaling components are further linked to pathways using the experimentally derived PPIs (protein-protein interactions) to reconstruct the human signaling pathways. Thus the map of the cross-talks via common signaling components and common signaling PPIs is conveniently inferred to provide valuable insights into the regulatory and cooperative relationships between signaling pathways. Lastly, gene ontology enrichment analysis is conducted to gain statistical knowledge about the reconstructed human signaling pathways.

Conclusions: Multi-label learning framework has been demonstrated effective in this work to model the phenomena that a signaling protein belongs to more than one signaling pathway. As results, novel signaling components and pathways targeted proteins are predicted to simultaneously reconstruct multiple human signaling pathways and the static map of their cross-talks for further biomedical research.

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Related in: MedlinePlus

a Matrix plots cross-talk ratio (%) of signaling components between the experimental human signaling pathways. b Matrix plots the cross-talk ratio (%) of signaling PPIs between the experimental human signaling pathways. c Matrix plots cross-talk ratio (%) of signaling components between the reconstructed human signaling pathways. d Matrix plots the cross-talk ratio (%) of signaling PPIs between the reconstructed human signaling pathways. The values along the diagonals are trivial and the color bar is used to highlight the magnitude of cross-talks between two human signaling pathways
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Fig1: a Matrix plots cross-talk ratio (%) of signaling components between the experimental human signaling pathways. b Matrix plots the cross-talk ratio (%) of signaling PPIs between the experimental human signaling pathways. c Matrix plots cross-talk ratio (%) of signaling components between the reconstructed human signaling pathways. d Matrix plots the cross-talk ratio (%) of signaling PPIs between the reconstructed human signaling pathways. The values along the diagonals are trivial and the color bar is used to highlight the magnitude of cross-talks between two human signaling pathways

Mentions: In general, signaling pathways temporally and spatially communicate via common signaling components and common signaling PPIs. Take the experimental NetPath database for example, EGFR signaling pathway shares 108 common signaling components with Interleukin signaling pathway and 106 common signaling components with TCR signaling pathway. To measure the relatedness of any two signaling pathways, we define two cross-talk ratios: the cross-talk ratio of signaling components (CTRSC) and the cross-talk ratio of signaling PPIs (CTRSPPI). Assume ASC and BSC to denote the sets of signaling components of two signaling pathway A and B, then CTRSC is defined as CTRSC = /ASC ∩ BSC///ASC ∪ BSC/, where /A/ denotes the cardinality of set A. CTRSC is actually the ratio of the overlap between set ASC and set BSC. The cross-talk ratio of signaling components (CTRSC)that is derived from the experimental NetPath database is illustrated in Fig. 1(a). We see that there generally are a certain number of signaling components shared between any two signaling pathways. Take TCR for instance, TCR seems to be more correlated with IL (CTRSC = 18.7 %), BCR (CTRSC = 23.9 %), EGFR (CTRSC = 18.1 %) and Kit (CTRSC = 18.2 %).Fig. 1


Multi-label multi-instance transfer learning for simultaneous reconstruction and cross-talk modeling of multiple human signaling pathways.

Mei S, Zhu H - BMC Bioinformatics (2015)

a Matrix plots cross-talk ratio (%) of signaling components between the experimental human signaling pathways. b Matrix plots the cross-talk ratio (%) of signaling PPIs between the experimental human signaling pathways. c Matrix plots cross-talk ratio (%) of signaling components between the reconstructed human signaling pathways. d Matrix plots the cross-talk ratio (%) of signaling PPIs between the reconstructed human signaling pathways. The values along the diagonals are trivial and the color bar is used to highlight the magnitude of cross-talks between two human signaling pathways
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4697333&req=5

Fig1: a Matrix plots cross-talk ratio (%) of signaling components between the experimental human signaling pathways. b Matrix plots the cross-talk ratio (%) of signaling PPIs between the experimental human signaling pathways. c Matrix plots cross-talk ratio (%) of signaling components between the reconstructed human signaling pathways. d Matrix plots the cross-talk ratio (%) of signaling PPIs between the reconstructed human signaling pathways. The values along the diagonals are trivial and the color bar is used to highlight the magnitude of cross-talks between two human signaling pathways
Mentions: In general, signaling pathways temporally and spatially communicate via common signaling components and common signaling PPIs. Take the experimental NetPath database for example, EGFR signaling pathway shares 108 common signaling components with Interleukin signaling pathway and 106 common signaling components with TCR signaling pathway. To measure the relatedness of any two signaling pathways, we define two cross-talk ratios: the cross-talk ratio of signaling components (CTRSC) and the cross-talk ratio of signaling PPIs (CTRSPPI). Assume ASC and BSC to denote the sets of signaling components of two signaling pathway A and B, then CTRSC is defined as CTRSC = /ASC ∩ BSC///ASC ∪ BSC/, where /A/ denotes the cardinality of set A. CTRSC is actually the ratio of the overlap between set ASC and set BSC. The cross-talk ratio of signaling components (CTRSC)that is derived from the experimental NetPath database is illustrated in Fig. 1(a). We see that there generally are a certain number of signaling components shared between any two signaling pathways. Take TCR for instance, TCR seems to be more correlated with IL (CTRSC = 18.7 %), BCR (CTRSC = 23.9 %), EGFR (CTRSC = 18.1 %) and Kit (CTRSC = 18.2 %).Fig. 1

Bottom Line: The predicted signaling components are further linked to pathways using the experimentally derived PPIs (protein-protein interactions) to reconstruct the human signaling pathways.Multi-label learning framework has been demonstrated effective in this work to model the phenomena that a signaling protein belongs to more than one signaling pathway.As results, novel signaling components and pathways targeted proteins are predicted to simultaneously reconstruct multiple human signaling pathways and the static map of their cross-talks for further biomedical research.

View Article: PubMed Central - PubMed

Affiliation: Software College, Shenyang Normal University, Shenyang, China. meisygle@gmail.com.

ABSTRACT

Background: Signaling pathways play important roles in the life processes of cell growth, cell apoptosis and organism development. At present the signal transduction networks are far from complete. As an effective complement to experimental methods, computational modeling is suited to rapidly reconstruct the signaling pathways at low cost. To our knowledge, the existing computational methods seldom simultaneously exploit more than three signaling pathways into one predictive model for the discovery of novel signaling components and the cross-talk modeling between signaling pathways.

Results: In this work, we propose a multi-label multi-instance transfer learning method to simultaneously reconstruct 27 human signaling pathways and model their cross-talks. Computational results show that the proposed method demonstrates satisfactory multi-label learning performance and rational proteome-wide predictions. Some predicted signaling components or pathway targeted proteins have been validated by recent literature. The predicted signaling components are further linked to pathways using the experimentally derived PPIs (protein-protein interactions) to reconstruct the human signaling pathways. Thus the map of the cross-talks via common signaling components and common signaling PPIs is conveniently inferred to provide valuable insights into the regulatory and cooperative relationships between signaling pathways. Lastly, gene ontology enrichment analysis is conducted to gain statistical knowledge about the reconstructed human signaling pathways.

Conclusions: Multi-label learning framework has been demonstrated effective in this work to model the phenomena that a signaling protein belongs to more than one signaling pathway. As results, novel signaling components and pathways targeted proteins are predicted to simultaneously reconstruct multiple human signaling pathways and the static map of their cross-talks for further biomedical research.

Show MeSH
Related in: MedlinePlus