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DeBi: Discovering Differentially Expressed Biclusters using a Frequent Itemset Approach
2011
Algorithms for Molecular Biology
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Results: Here we present a fast biclustering algorithm called DeBi (Differentially Expressed BIclusters). The algorithm is based on a well known data mining approach called frequent itemset. ...
Biclustering overcomes these limitations by grouping genes and samples simultaneously. It discovers subsets of genes that are co-expressed in certain samples. ...
Here, we propose a novel, fast biclustering algorithm called DeBi that utilizes differential gene expression analysis. In DeBi, a bicluster has the following two main properties. ...
doi:10.1186/1748-7188-6-18
pmid:21699691
pmcid:PMC3152888
fatcat:ctqigwhiv5c67irap2nesqzovm
BicPAM: Pattern-based biclustering for biomedical data analysis
2014
Algorithms for Molecular Biology
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Given an itemset database D and a minimum support threshold θ, the frequent itemset mining (FIM) problem consists of computing the set {P | P ⊆ L, sup P ≥ θ}. ...
Contrasting, recent biclustering approaches relying on pattern mining methods can exhaustively discover flexible structures of robust biclusters. ...
(P)| ≥ θ; -A closed frequent itemset is a frequent itemset with no superset with same support ∀ P ⊃P |P | < |P| ; -A maximal frequent itemset is a frequent itemset with all supersents being infrequent, ...
doi:10.1186/s13015-014-0027-z
pmid:25649207
pmcid:PMC4302537
fatcat:ucjj6bxwxjfodipiwpz5qjzwg4
BicSPAM: flexible biclustering using sequential patterns
2014
BMC Bioinformatics
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Biclustering is a critical task for biomedical applications. ...
Additionally, they are not able to discover biclusters with symmetries and parameterizable levels of noise. ...
Another option is to rely on frequent itemset mining [22] [23] [24] [25] [26] . ...
doi:10.1186/1471-2105-15-130
pmid:24885271
pmcid:PMC4071222
fatcat:d4mqygeemrblbgx7pqud2xkul4
Contributions to Biclustering of Microarray Data Using Formal Concept Analysis
[article]
2018
arXiv
pre-print
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Biclustering is an unsupervised data mining technique that aims to unveil patterns (biclusters) from gene expression data matrices. ...
In the framework of this thesis, we propose new biclustering algorithms for microarray data. The latter is done using data mining techniques. ...
In [Mondal et al., 2012] , the authors proposed a new approach, called FIST, for extracting bases of extended association rules and conceptual biclusters, using frequent closed itemsets [Pasquier et ...
arXiv:1811.09562v1
fatcat:koql6oifrvgsnk6j56bpne6jvm
BicPAMS: software for biological data analysis with pattern-based biclustering
2017
BMC Bioinformatics
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Biclustering has been largely applied for the unsupervised analysis of biological data, being recognised today as a key technique to discover putative modules in both expression data (subsets of genes ...
Methods: To enable the effective use of pattern-based biclustering by the scientific community, we developed BicPAMS (Biclustering based on PAttern Mining Software), a software that: 1) makes available ...
Frequent itemset miners (FIM) are selected for the remaining coherency assumptions. ...
doi:10.1186/s12859-017-1493-3
pmid:28153040
pmcid:PMC5290636
fatcat:iblmsccv4fdklamizdepehql5a
A systematic comparative evaluation of biclustering techniques
2017
BMC Bioinformatics
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Biclustering techniques are capable of simultaneously clustering rows and columns of a data matrix. ...
These techniques became very popular for the analysis of gene expression data, since a gene can take part of multiple biological pathways which in turn can be active only under specific experimental conditions ...
] , an algorithm based on a frequent itemset approach that applies a depthfirst traversal on an enumeration tree to discover hidden patterns in data. ...
doi:10.1186/s12859-017-1487-1
pmid:28114903
pmcid:PMC5259837
fatcat:aqwpz3ln4vajfo3ufcrj7gz5fm
BicNET: Flexible module discovery in large-scale biological networks using biclustering
2016
Algorithms for Molecular Biology
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Methods: This work proposes Biclustering NETworks (BicNET), a biclustering algorithm to discover non-trivial yet coherent modules in weighted biological networks with heightened efficiency. ...
First, we motivate the relevance of discovering network modules given by constant, symmetric, plaid and order-preserving biclustering models. ...
SCM was also partially funded by the EURIAS Fellowship Programme and the European Commission (Marie-Sklodowska-Curie actions CoFUND Programme-FP7) through a grant for a junior fellowship position at Istituto ...
doi:10.1186/s13015-016-0074-8
pmid:27213009
pmcid:PMC4875761
fatcat:54uw36eqafdabel2rjjdrvniqa
TuBA: Tunable biclustering algorithm reveals clinically relevant tumor transcriptional profiles in breast cancer
2019
GigaScience
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We propose a graph-based Tunable Biclustering Algorithm (TuBA) based on a novel pairwise proximity measure, examining the relationship of samples at the extremes of genes' expression profiles to identify ...
Traditional clustering approaches for gene expression data are not well adapted to address the complexity and heterogeneity of tumors, where small sets of genes may be aberrantly co-expressed in specific ...
In their paper on DeBi [55] , a novel biclustering method that identifies differentially expressed biclusters based on a frequent itemset approach, Serin and Vingron applied their method to both synthetic ...
doi:10.1093/gigascience/giz064
pmid:31216036
pmcid:PMC6582332
fatcat:cnrmoegnqfgbjb6nhioy6rs5lq
Biclustering analysis for large scale data
[article]
2012
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Our algorithm aims to find biclusters where each gene in a bicluster should be highly or lowly expressed over all the bicluster samples compared to the rest of the samples. ...
It is shown that the DeBi algorithm provides biologically significant biclust [...] ...
In Chapter 5, we will introduce our novel fast biclustering algorithm called DeBi (Differentially Expressed BIclusters). ...
doi:10.17169/refubium-17150
fatcat:rm4fbauyhfcilhaloluaev4xia
Identification of Differentially Expressed Gene Modules in Heterogeneous Diseases
2021
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They are capable of identifying genes with a similar expression pattern in a previously unknown subset of samples. After an overview [...] ...
The investigation of complex diseases not only brings us closer to the understanding of their mechanisms but also yields a number of useful intermediate results, e.g. the discovery of clinically relevant ...
The method binarizes the expression matrix and utilizes the Frequent Itemset Approach (MAFIA) [32] algorithm to detect maximal binary biclusters. 2. Expanding seed biclusters. ...
doi:10.4119/unibi/2954162
fatcat:56wt3qyycbapfldgqeir45xzj4