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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

Cross-talks between TGF-βsignaling pathway and TNF-αsignaling pathway (target instance). The nodes and edges in green denote the predicted signaling components and derived signaling PPIs of TGF-βsignaling pathway. The nodes and edges in blue denote the predicted signaling components and derived signaling PPIs of TNF-αsignaling pathway. The nodes and edges in red denote the common signaling components and the common signaling PPIs
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Fig6: Cross-talks between TGF-βsignaling pathway and TNF-αsignaling pathway (target instance). The nodes and edges in green denote the predicted signaling components and derived signaling PPIs of TGF-βsignaling pathway. The nodes and edges in blue denote the predicted signaling components and derived signaling PPIs of TNF-αsignaling pathway. The nodes and edges in red denote the common signaling components and the common signaling PPIs

Mentions: The static map of cross-talks between TGF-β signaling pathway and TNF-αsignaling pathway (target instance) is illustrated in Fig. 6, where the color green denotes TGF-βsignaling components and signaling PPIs, the color blue denotes TNF-α signaling components and signaling PPIs, and the color red denotes the cross-talk signaling components and the cross-talk signaling PPIs. There are 52 cross-talk signaling components and 6 cross-talk signaling PPIs between TGF-β signaling pathway and TNF-α signaling pathway, of which 6 cross-talk signaling components and the 6 cross-talk signaling PPIs are predicted. From Fig. 6, we can see that most of the cross-talk signaling components are peripheral proteins at the cross boundaries of the two signaling pathways except several hub proteins (e.g. TGF-β: SMAD2, SMAD3, JUNB; TNF-α: MAP3K, HSPA8, IKBKB, etc.).Fig. 6


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)

Cross-talks between TGF-βsignaling pathway and TNF-αsignaling pathway (target instance). The nodes and edges in green denote the predicted signaling components and derived signaling PPIs of TGF-βsignaling pathway. The nodes and edges in blue denote the predicted signaling components and derived signaling PPIs of TNF-αsignaling pathway. The nodes and edges in red denote the common signaling components and the common signaling PPIs
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig6: Cross-talks between TGF-βsignaling pathway and TNF-αsignaling pathway (target instance). The nodes and edges in green denote the predicted signaling components and derived signaling PPIs of TGF-βsignaling pathway. The nodes and edges in blue denote the predicted signaling components and derived signaling PPIs of TNF-αsignaling pathway. The nodes and edges in red denote the common signaling components and the common signaling PPIs
Mentions: The static map of cross-talks between TGF-β signaling pathway and TNF-αsignaling pathway (target instance) is illustrated in Fig. 6, where the color green denotes TGF-βsignaling components and signaling PPIs, the color blue denotes TNF-α signaling components and signaling PPIs, and the color red denotes the cross-talk signaling components and the cross-talk signaling PPIs. There are 52 cross-talk signaling components and 6 cross-talk signaling PPIs between TGF-β signaling pathway and TNF-α signaling pathway, of which 6 cross-talk signaling components and the 6 cross-talk signaling PPIs are predicted. From Fig. 6, we can see that most of the cross-talk signaling components are peripheral proteins at the cross boundaries of the two signaling pathways except several hub proteins (e.g. TGF-β: SMAD2, SMAD3, JUNB; TNF-α: MAP3K, HSPA8, IKBKB, etc.).Fig. 6

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