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DeBi: Discovering Differentially Expressed Biclusters using a Frequent Itemset Approach

Akdes Serin, Martin Vingron
2011 Algorithms for Molecular Biology  
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

Rui Henriques, Sara C Madeira
2014 Algorithms for Molecular Biology  
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

Rui Henriques, Sara C Madeira
2014 BMC Bioinformatics  
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]

Amina Houari
2018 arXiv   pre-print
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

Rui Henriques, Francisco L. Ferreira, Sara C. Madeira
2017 BMC Bioinformatics  
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

Victor A. Padilha, Ricardo J. G. B. Campello
2017 BMC Bioinformatics  
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

Rui Henriques, Sara C. Madeira
2016 Algorithms for Molecular Biology  
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

Amartya Singh, Gyan Bhanot, Hossein Khiabanian
2019 GigaScience  
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]

Akdes Serin, Universitätsbibliothek Der FU Berlin, Universitätsbibliothek Der FU Berlin
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

Olga Zolotareva
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