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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

Reconstruction of Notch signaling pathway (target instance). The nodes and edges in green denote the signaling components and signaling PPIs of the experimental Notch signaling pathway. The nodes and edges in red denote the predicted signaling components and the derived signaling PPIs
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Fig3: Reconstruction of Notch signaling pathway (target instance). The nodes and edges in green denote the signaling components and signaling PPIs of the experimental Notch signaling pathway. The nodes and edges in red denote the predicted signaling components and the derived signaling PPIs

Mentions: Signaling proteins generally do not work in isolation but transmit signal via interaction with other proteins or biological molecules. The predicted signaling components needed to be linked to the current human signaling pathways via experimental or predicted protein-protein interactions. For the sake of reliability, we use the experimental PPIs from HPRD database [34] to link the predicted signaling components. Once a predicted signaling component is linked to a signaling pathway, the corresponding PPI becomes a novel signaling PPI of the signaling pathway. Here novel signaling PPI does not mean the PPI is newly predicted, but mean that the PPI is newly treated as a part of the signaling pathway. From HPRD database, we obtain two kinds of signaling PPIs: (1) the PPIs between the predicted signaling components and the known signaling components; (2) the PPIs between the predicted signaling components. The derived signaling PPIs are given in Additional file 4 (target instance) and Additional file 5 (homolog instance). The number of novel signaling PPIs for each signaling pathway is shown in Table 1. Here we link the predicted signaling components to the current signaling pathways via experimental PPIs. We only illustrate Notch, TGF-β and TNF-α signaling pathways that are predicted by the target instances as examples (see Figs. 3, 4 and 5).Fig. 3


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)

Reconstruction of Notch signaling pathway (target instance). The nodes and edges in green denote the signaling components and signaling PPIs of the experimental Notch signaling pathway. The nodes and edges in red denote the predicted signaling components and the derived signaling PPIs
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig3: Reconstruction of Notch signaling pathway (target instance). The nodes and edges in green denote the signaling components and signaling PPIs of the experimental Notch signaling pathway. The nodes and edges in red denote the predicted signaling components and the derived signaling PPIs
Mentions: Signaling proteins generally do not work in isolation but transmit signal via interaction with other proteins or biological molecules. The predicted signaling components needed to be linked to the current human signaling pathways via experimental or predicted protein-protein interactions. For the sake of reliability, we use the experimental PPIs from HPRD database [34] to link the predicted signaling components. Once a predicted signaling component is linked to a signaling pathway, the corresponding PPI becomes a novel signaling PPI of the signaling pathway. Here novel signaling PPI does not mean the PPI is newly predicted, but mean that the PPI is newly treated as a part of the signaling pathway. From HPRD database, we obtain two kinds of signaling PPIs: (1) the PPIs between the predicted signaling components and the known signaling components; (2) the PPIs between the predicted signaling components. The derived signaling PPIs are given in Additional file 4 (target instance) and Additional file 5 (homolog instance). The number of novel signaling PPIs for each signaling pathway is shown in Table 1. Here we link the predicted signaling components to the current signaling pathways via experimental PPIs. We only illustrate Notch, TGF-β and TNF-α signaling pathways that are predicted by the target instances as examples (see Figs. 3, 4 and 5).Fig. 3

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