Journal article
RUBic: rapid unsupervised biclustering
- Abstract:
- Biclustering of biologically meaningful binary information is essential in many applications related to drug discovery, like protein-protein interactions and gene expressions. However, for robust performance in recently emerging large health datasets, it is important for new biclustering algorithms to be scalable and fast. We present a rapid unsupervised biclustering (RUBic) algorithm that achieves this objective with a novel encoding and search strategy. RUBic significantly reduces the computational overhead on both synthetic and experimental datasets shows significant computational benefits, with respect to several state-of-the-art biclustering algorithms. In 100 synthetic binary datasets, our method took ~71.1s to extract 494,872 biclusters. In the human PPI database of size 4085x4085, our method generates 1840 biclusters in ~48.6s. On a central nervous system embryonic tumor gene expression dataset of size 712,940, our algorithm takes 101 min to produce 747,069 biclusters, while the recent competing algorithms take significantly more time to produce the same result. RUBic is also evaluated on five different gene expression datasets and shows significant speed-up in execution time with respect to existing approaches to extract significant KEGG-enriched bi-clustering. RUBic can operate on two modes, base and flex, where base mode generates maximal biclusters and flex mode generates less number of clusters and faster based on their biological significance with respect to KEGG pathways. The code is available at ( https://github.com/CMATERJU-BIOINFO/RUBic ) for academic use only.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 3.4MB, Terms of use)
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- Publisher copy:
- 10.1186/s12859-023-05534-3
Authors
- Publisher:
- BioMed Central
- Journal:
- BMC Bioinformatics More from this journal
- Volume:
- 24
- Issue:
- 1
- Article number:
- 435
- Place of publication:
- England
- Publication date:
- 2023-11-16
- Acceptance date:
- 2023-10-16
- DOI:
- EISSN:
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1471-2105
- Pmid:
-
37974081
- Language:
-
English
- Keywords:
- Pubs id:
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2113644
- Local pid:
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pubs:2113644
- Deposit date:
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2025-04-15
- ARK identifier:
Terms of use
- Copyright holder:
- Sriwastava et al.
- Copyright date:
- 2023
- Rights statement:
- © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
- Licence:
- CC Attribution (CC BY)
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