ToBio : Global Pathway Similarity Search based on Topological and Biological Features

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

4 Scopus Citations
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Original languageEnglish
Pages (from-to)336-349
Journal / PublicationIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number1
Online published3 Nov 2017
Publication statusPublished - Jan 2019


Pathway similarity search plays a vital role in the post-genomics era. Unfortunately, pathway similarity search involves the graph isomorphism problem which is NP-complete. Therefore, efficient search algorithms are desirable. In this work, we propose a novel global pathway similarity search approach named ToBio, which considers both topological and biological features for effective global pathway similarity search. Specifically, as motivated from nature, various topological and biological features including subgraph signature similarities, sequence similarities, and gene ontology similarities are considered in ToBio. Since different features carry different functional importance and dependences, we report three schemes of ToBio using different sets of features. In addition, to enhance the existing search algorithms for rigorous comparisons, post-processing pipelines are also proposed to investigate how different features can contribute to the search performance. ToBio and other state-of-the-art methods are benchmarked on the gold-standard pathway datasets from three species; the results demonstrate the competitive edges of ToBio over the state-of-the-arts ranging from the topological aspects to the biological aspects. Case studies have been conducted to reveal mechanistic insights into the unique search performance of ToBio.

Research Area(s)

  • biological network, Biological system modeling, BLAST score, Databases, GO annotation, Pathway, Proteins, random forest regression, Search problems, Semantics, subgraph signature