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BiC2PAM: constraint-guided biclustering for biological data analysis with domain knowledge

Overview of attention for article published in Algorithms for Molecular Biology, September 2016
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Title
BiC2PAM: constraint-guided biclustering for biological data analysis with domain knowledge
Published in
Algorithms for Molecular Biology, September 2016
DOI 10.1186/s13015-016-0085-5
Pubmed ID
Authors

Rui Henriques, Rui Henriques, Sara C. Madeira

Abstract

Biclustering has been largely used in biological data analysis, enabling the discovery of putative functional modules from omic and network data. Despite the recognized importance of incorporating domain knowledge to guide biclustering and guarantee a focus on relevant and non-trivial biclusters, this possibility has not yet been comprehensively addressed. This results from the fact that the majority of existing algorithms are only able to deliver sub-optimal solutions with restrictive assumptions on the structure, coherency and quality of biclustering solutions, thus preventing the up-front satisfaction of knowledge-driven constraints. Interestingly, in recent years, a clearer understanding of the synergies between pattern mining and biclustering gave rise to a new class of algorithms, termed as pattern-based biclustering algorithms. These algorithms, able to efficiently discover flexible biclustering solutions with optimality guarantees, are thus positioned as good candidates for knowledge incorporation. In this context, this work aims to bridge the current lack of solid views on the use of background knowledge to guide (pattern-based) biclustering tasks. This work extends (pattern-based) biclustering algorithms to guarantee the satisfiability of constraints derived from background knowledge and to effectively explore efficiency gains from their incorporation. In this context, we first show the relevance of constraints with succinct, (anti-)monotone and convertible properties for the analysis of expression data and biological networks. We further show how pattern-based biclustering algorithms can be adapted to effectively prune of the search space in the presence of such constraints, as well as be guided in the presence of biological annotations. Relying on these contributions, we propose BiClustering with Constraints using PAttern Mining (BiC2PAM), an extension of BicPAM and BicNET biclustering algorithms. Experimental results on biological data demonstrate the importance of incorporating knowledge within biclustering to foster efficiency and enable the discovery of non-trivial biclusters with heightened biological relevance. This work provides the first comprehensive view and sound algorithm for biclustering biological data with constraints derived from user expectations, knowledge repositories and/or literature.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 11%
Unknown 8 89%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 22%
Researcher 2 22%
Lecturer 1 11%
Student > Ph. D. Student 1 11%
Professor > Associate Professor 1 11%
Other 0 0%
Unknown 2 22%
Readers by discipline Count As %
Computer Science 4 44%
Biochemistry, Genetics and Molecular Biology 2 22%
Unknown 3 33%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 September 2016.
All research outputs
#6,391,336
of 8,422,812 outputs
Outputs from Algorithms for Molecular Biology
#102
of 159 outputs
Outputs of similar age
#178,281
of 253,833 outputs
Outputs of similar age from Algorithms for Molecular Biology
#2
of 3 outputs
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