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 al...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-11-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-023-05534-3 |
| _version_ | 1797556108106137600 |
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| author | Brijesh K. Sriwastava Anup Kumar Halder Subhadip Basu Tapabrata Chakraborti |
| author_facet | Brijesh K. Sriwastava Anup Kumar Halder Subhadip Basu Tapabrata Chakraborti |
| author_sort | Brijesh K. Sriwastava |
| collection | DOAJ |
| description | 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 $$\sim 71.1$$ ∼ 71.1 s to extract 494,872 biclusters. In the human PPI database of size $$4085\times 4085$$ 4085 × 4085 , our method generates 1840 biclusters in $$\sim 48.6$$ ∼ 48.6 s. 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. |
| first_indexed | 2024-03-10T16:57:05Z |
| format | Article |
| id | doaj.art-9c652dffadeb4f478820d968df4dcfdf |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-03-10T16:57:05Z |
| publishDate | 2023-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-9c652dffadeb4f478820d968df4dcfdf2023-11-20T11:06:07ZengBMCBMC Bioinformatics1471-21052023-11-0124111610.1186/s12859-023-05534-3RUBic: rapid unsupervised biclusteringBrijesh K. Sriwastava0Anup Kumar Halder1Subhadip Basu2Tapabrata Chakraborti3Computer Science and Engineering Department, Government College of Engineering and Leather TechnologyFaculty of Mathematics and Information Sciences, Warsaw University of TechnologyDepartment of Computer Science and Engineering, Jadavpur UniversityThe Alan Turing Institute and University College LondonAbstract 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 $$\sim 71.1$$ ∼ 71.1 s to extract 494,872 biclusters. In the human PPI database of size $$4085\times 4085$$ 4085 × 4085 , our method generates 1840 biclusters in $$\sim 48.6$$ ∼ 48.6 s. 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.https://doi.org/10.1186/s12859-023-05534-3Data miningAlgorithm design and analysisBiclustering algorithmsComputational complexity |
| spellingShingle | Brijesh K. Sriwastava Anup Kumar Halder Subhadip Basu Tapabrata Chakraborti RUBic: rapid unsupervised biclustering BMC Bioinformatics Data mining Algorithm design and analysis Biclustering algorithms Computational complexity |
| title | RUBic: rapid unsupervised biclustering |
| title_full | RUBic: rapid unsupervised biclustering |
| title_fullStr | RUBic: rapid unsupervised biclustering |
| title_full_unstemmed | RUBic: rapid unsupervised biclustering |
| title_short | RUBic: rapid unsupervised biclustering |
| title_sort | rubic rapid unsupervised biclustering |
| topic | Data mining Algorithm design and analysis Biclustering algorithms Computational complexity |
| url | https://doi.org/10.1186/s12859-023-05534-3 |
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