Abstract
Identifying topological relationships among multiple entities in biological networks is critical towards the understanding of the organizational principles of network functionality. Theoretically, this problem can be solved using minimum Steiner tree (MSTT) algorithms. However, due to large network size, it remains to be computationally challenging, and the predictive value of multi-entity topological relationships is still unclear. We present a novel solution called Cluster-based Steiner Tree Miner (CST-Miner) to instantly identify multi-entity topological relationships in biological networks. Given a list of user-specific entities, CST-Miner decomposes a biological network into nested cluster-based subgraphs, on which multiple minimum Steiner trees are identified. By merging all of them into a minimum cost tree, the optimal topological relationships among all the user-specific entities are revealed. Experimental results showed that CST-Miner can finish in nearly log-linear time and the tree constructed by CST-Miner is close to the global minimum.
Original language | English |
---|---|
Article number | 8666735 |
Pages (from-to) | 769-776 |
Number of pages | 8 |
Journal | IEEE/ACM Transactions on Computational Biology and Bioinformatics |
Volume | 17 |
Issue number | 3 |
DOIs | |
State | Published - May 1 2020 |
Bibliographical note
Publisher Copyright:© 2004-2012 IEEE.
Keywords
- Biological network
- clustering
- minimum steiner tree
- topological relationship
ASJC Scopus subject areas
- Biotechnology
- Genetics
- Applied Mathematics