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Volume 9, Issue 2
A Parallel Method for Querying Target Subnetwork in a Biomolecular Network

J. Xie, W. Zhang, S. Zhang & T. Wen

Int. J. Numer. Anal. Mod., 9 (2012), pp. 326-337.

Published online: 2012-09

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  • Abstract

Similarity analysis of biomolecular networks among different species or within one species is an efficient approach to understand evolution or disease. The more data from biological experiment, the larger networks. Sequential computational limitation on single PC or workstation have to be considered when methods are developed. The Immediate Neighbors-in-first Method is a method for querying the subnetwork which is most similar to the target in a biomolecular network. Parallel algorithm for it to treat large-scale networks is developed and the parallel performance is evaluated in this paper. Moreover, we apply the present method to two groups of tests on real biological data including protein interaction networks of Fly and Yeast and metabolic networks of Yeast and E. coli. Several conserved protein interactions and metabolic pathways are found and some new protein interactions and functions are predicted.

  • Keywords

Biomolecular network, network querying, parallel computing.

  • AMS Subject Headings

35R35, 49J40, 60G40

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{IJNAM-9-326, author = {J. Xie, W. Zhang, S. Zhang and T. Wen}, title = {A Parallel Method for Querying Target Subnetwork in a Biomolecular Network}, journal = {International Journal of Numerical Analysis and Modeling}, year = {2012}, volume = {9}, number = {2}, pages = {326--337}, abstract = {

Similarity analysis of biomolecular networks among different species or within one species is an efficient approach to understand evolution or disease. The more data from biological experiment, the larger networks. Sequential computational limitation on single PC or workstation have to be considered when methods are developed. The Immediate Neighbors-in-first Method is a method for querying the subnetwork which is most similar to the target in a biomolecular network. Parallel algorithm for it to treat large-scale networks is developed and the parallel performance is evaluated in this paper. Moreover, we apply the present method to two groups of tests on real biological data including protein interaction networks of Fly and Yeast and metabolic networks of Yeast and E. coli. Several conserved protein interactions and metabolic pathways are found and some new protein interactions and functions are predicted.

}, issn = {2617-8710}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/ijnam/631.html} }
TY - JOUR T1 - A Parallel Method for Querying Target Subnetwork in a Biomolecular Network AU - J. Xie, W. Zhang, S. Zhang & T. Wen JO - International Journal of Numerical Analysis and Modeling VL - 2 SP - 326 EP - 337 PY - 2012 DA - 2012/09 SN - 9 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/ijnam/631.html KW - Biomolecular network, network querying, parallel computing. AB -

Similarity analysis of biomolecular networks among different species or within one species is an efficient approach to understand evolution or disease. The more data from biological experiment, the larger networks. Sequential computational limitation on single PC or workstation have to be considered when methods are developed. The Immediate Neighbors-in-first Method is a method for querying the subnetwork which is most similar to the target in a biomolecular network. Parallel algorithm for it to treat large-scale networks is developed and the parallel performance is evaluated in this paper. Moreover, we apply the present method to two groups of tests on real biological data including protein interaction networks of Fly and Yeast and metabolic networks of Yeast and E. coli. Several conserved protein interactions and metabolic pathways are found and some new protein interactions and functions are predicted.

J. Xie, W. Zhang, S. Zhang and T. Wen. (2012). A Parallel Method for Querying Target Subnetwork in a Biomolecular Network. International Journal of Numerical Analysis and Modeling. 9 (2). 326-337. doi:
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