Aligning Multiple PPI Networks with Representation Learning on Networks

Song, Bo; Gao, Jianliang; Du, Hongliang; Chen, Zheng; Hu, Xiaohua

doi:10.1109/bibm.2018.8621084

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2019

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“…The quality of mapping between n1 and n2 can be represented as (4). (4) Similarity between the nodes can be expressed with a cost function α [27]. Mapping, R can be expressed as in (5) and A corresponds the Eigen vector.…”

Section: Other Alignersmentioning

confidence: 99%

Alignment of Protein Interaction Networks and Disease Prediction: A Survey

Ali¹

2019

IJATCSE

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Proteins interact each other to perform many cellular activities. These interactions can be considered as Protein Protein Interaction networks (PPI). Interacting proteins form protein complexes. Mapping nodes between networks is denoted as alignment. The main intention of network alignment approach is to identify the protein complexes, which in turn helps to identify the functionality of protein complexes in various cellular systems. These interactome units form the conserved pathways between the networks. So network alignment requires lot of attention and several algorithms and techniques have been proposed to address this. The study of PPI is widely recognized to know more about the underlying complex disease because proteins associated with any disease get connected and form subgraphs or pathways. In this paper, the authors compared the various aligners, the performance evaluation metrics, the common databases used for PPI evaluation and the importance of PPI network in biomedical research.

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Section: Other Alignersmentioning

confidence: 99%

Alignment of Protein Interaction Networks and Disease Prediction: A Survey

Ali¹

2019

IJATCSE

View full text Add to dashboard Cite

show abstract

Aligning Multiple PPI Networks with Representation Learning on Networks

Cited by 1 publication

References 25 publications

Alignment of Protein Interaction Networks and Disease Prediction: A Survey

Alignment of Protein Interaction Networks and Disease Prediction: A Survey

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