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BiGGEsTS: integrated environment for biclustering analysis of time series gene expression data
2009
BMC Research Notes
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Conclusions: BiGGEsTS is a free open source graphical software tool for revealing local coexpression of genes in specific intervals of time, while integrating meaningful information on gene annotations ...
Gene Ontology (GO) annotations are used to assess the biological relevance of the biclusters. Methods for preprocessing expression time series and post-processing results are also included. ...
BiGGEsTS (BiclusterinG Gene Expression Time Series) is a free and open source graphical application using state-of-the-art biclustering algorithms specifically developed for analyzing gene expression time ...
doi:10.1186/1756-0500-2-124
pmid:19583847
pmcid:PMC2720980
fatcat:h3zokoieh5hwfa7vcpezabuyhy
Hierarchical Generative Biclustering for MicroRNA Expression Analysis
2011
Journal of Computational Biology
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We show that the model outperforms other four biclustering procedures in a large miRNA data set. ...
Clustering methods are a useful and common first step in gene expression studies, but the results may be hard to interpret. ...
We thank Leo Lahti for helpful comments. This work was supported by the Finnish Funding Agency for Technology and Innovation ...
doi:10.1089/cmb.2010.0256
pmid:21385032
fatcat:k35p5b7atvbkjpehyrrd3zwlf4
Hierarchical Generative Biclustering for MicroRNA Expression Analysis
[chapter]
2010
Lecture Notes in Computer Science
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We show that the model outperforms other four biclustering procedures in a large miRNA data set. ...
Clustering methods are a useful and common first step in gene expression studies, but the results may be hard to interpret. ...
We thank Leo Lahti for helpful comments. This work was supported by the Finnish Funding Agency for Technology and Innovation ...
doi:10.1007/978-3-642-12683-3_5
fatcat:5in23a7dqrb7zfzpvbclmomkji
BIDEAL: A Toolbox for Bicluster Analysis—Generation, Visualization and Validation
2021
SN Computer Science
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The biclusters of these datasets have been generated using BIDEAL and evaluated in terms of coherency, differential co-expression ranking, and similarity measure. ...
A single toolbox comprising various biclustering algorithms play a vital role to extract meaningful patterns from the data for detecting diseases, biomarkers, gene-drug association, etc. ...
Information retrieval from data mainly depends on the type of local patterns, whether it has overlapping and constant biclusters, or noisy data. ...
doi:10.1007/s42979-020-00411-9
fatcat:w4vcq6atdnfdngph7jcvxbp2p4
BIDEAL: A Toolbox for Bicluster Analysis – Generation, Visualization and Validation
[article]
2020
arXiv
pre-print
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The biclusters of these datasets have been generated using BIDEAL and evaluated in terms of coherency, differential co-expression ranking, and similarity measure. ...
A single toolbox comprising various biclustering algorithms play a vital role to extract meaningful patterns from the data for detecting diseases, biomarkers, gene-drug association, etc. ...
Information retrieval from data mainly depends on the type of local patterns, whether it has overlapping and constant biclusters, or noisy data. ...
arXiv:2007.13737v1
fatcat:5g73flwisradxjhja4ci7sjs2q
Construction of gene regulatory networks using biclustering and bayesian networks
2011
Theoretical Biology and Medical Modelling
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Time series transcriptomic data measured by genome-wide DNA microarrays are traditionally used for GRN modelling. ...
Results: In this paper, we develop a biclustering function enrichment analysis toolbox (BicAT-plus) to study the effect of biclustering in reducing data dimensions. ...
We also thank Stanford Microarray Database for making microarray data available and the lab members for the courteous help they gave us. ...
doi:10.1186/1742-4682-8-39
pmid:22018164
pmcid:PMC3231811
fatcat:oawom2bkhvdotoumkxnyugdxka
ComiRNet: a web-based system for the analysis of miRNA-gene regulatory networks
2015
BMC Bioinformatics
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miRNA-gene target interaction data and for the discovery of miRNA functions and mechanisms. ...
Conclusions: ComiRNet represents a valuable resource for elucidating the miRNAs' role in complex biological processes by exploiting data on their putative function in the context of MGRNs. ...
The authors also wish to thank Raffaele Ciliberti for his support in the development of the web-based system and Lynn Rudd for her help in reading the manuscript. ...
doi:10.1186/1471-2105-16-s9-s7
pmid:26051695
pmcid:PMC4464030
fatcat:6z4aqe4ugvaiditemw7alduhi4
Comparative Microbial Modules Resource: Generation and Visualization of Multi-species Biclusters
2011
PLoS Computational Biology
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We illustrate the use of our system by exploring conserved biclusters involved in nitrogen metabolism, uncovering a putative function for yjjI, a currently uncharacterized gene that we predict to be involved ...
We provide an example use of our system for two bacteria, Escherichia coli and Salmonella Typhimurium. ...
overlapping gene members N Plots: Bicluster plots for gene expression profiles, mean gene expression, and expression heatmap
Figure 5 . 5 CMMR linked Gaggled tools I: Gene Network, Data Matrix Viewer ...
doi:10.1371/journal.pcbi.1002228
pmid:22144874
pmcid:PMC3228777
fatcat:xabcnfbvjfedbcvba4kabuf23q
Identification of microRNA regulatory modules in Arabidopsis via a probabilistic graphical model
2008
Computer applications in the biosciences : CABIOS
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Here, we propose a method based on the probabilistic graphical model to identify the regulatory modules of miRNAs and the core regulatory motifs involved in their ability to regulate gene expression. ...
Motivation: MicroRNAs (miRNAs) play important roles in gene regulation and are regarded as key components in gene regulatory pathways. ...
ACKNOWLEDGEMENTS The authors would like to thank Dr Jeong-Ho Chang for helpful discussion and Dr Jim Giovannoni for critical review of this article. ...
doi:10.1093/bioinformatics/btn626
pmid:19056778
fatcat:355b5kjwhngtpnsrcdnl222moi
BAYESIAN BICLUSTERING FOR PATIENT STRATIFICATION
2016
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
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To address these issues, we introduce a novel integrative Bayesian biclustering method, called B2PS, for patient stratification and propose methods for evaluating the results. ...
First, it is still unclear if integrating different datatypes will help in detecting disease subtypes more accurately, and, if not, which datatype(s) are most useful for this task. ...
Clinical data were also available for the patients and contained information required for survival analysis. We retrieved gene ontology data for GOTO analysis using the 'biomaRt' R package [22] . ...
pmid:26776199
fatcat:dzkfpl3o35dknluxshexgmk7hm
Bottom-Up Biclustering of Expression Data
2006
2006 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology
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In a gene expression data matrix a bicluster is a sub-matrix of genes and conditions that exhibits a high correlation of expression activity across both rows and columns. ...
Biclustering has the potential to retrieve these local signals and also to model overlapping groups of genes. ...
The bicluster model of gene expression data In general biclustering refers to the 'simultaneous clustering' of both rows and columns of a data matrix [14] . ...
doi:10.1109/cibcb.2006.330995
fatcat:3nidgcvumfaqvg7uflsup3tx6u
Genetic algorithm based detection of general linear biclusters
2014
2014 International Conference on Machine Learning and Cybernetics
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Biclustering simultaneously groups on both rows and columns of a data matrix and has been applied to various fields, especially gene expression data. ...
The performance of our algorithm is tested via simulated data, gene expression data and compared with several other bicluster methods. ...
It has many applications to different fields such as text mining and information retrieval [3] , economic data analysis [4, 5] , biological data analysis [14, 16, 30] , etc. ...
doi:10.1109/icmlc.2014.7009667
dblp:conf/icmlc/ToL14
fatcat:3qth6fummvd3zemfai2rhq5sl4
Extraction of Biological Knowledge by Clustering Data Mining Techniques
2018
International Journal for Research in Applied Science and Engineering Technology
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These procedures, beginning from numerous sources, for example, the aftereffects of high throughput tests or clinical records, goes for unveiling already obscure information and connections. I. ...
Bioinformatics and computational science include the utilization of systems in-cluding connected arithmetic, informatics, measurements, software engineering, arti-ficial insight, science, and organic chemistry ...
Furthermore, a biclustering model can avoid those noise genes that are not active in any experimental condition. Biclustering of microarray data was first introduced in . ...
doi:10.22214/ijraset.2018.7118
fatcat:fy6slr46aredfhnhdjuryqng6q
Biclustering Analysis for Pattern Discovery: Current Techniques, Comparative Studies and Applications
2012
Current Bioinformatics
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We present performance evaluation results for several of the well known biclustering algorithms, on both artificial and real gene expression datasets. ...
directions in a data matrix. ...
In [46] , Gu and Liu proposed a fully generative models called Bayesian biclustering algorithm (BBC) for gene expression data. ...
doi:10.2174/157489312799304413
fatcat:aqtxfft43ngxvf4rm2znuvse7i
Analysis of Gene Expression Data Using Biclustering Algorithms
[chapter]
2012
Functional Genomics
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Biclusters Inclusion Maximal (Bimax) Bimax [11] is a simple binary model and new fast divide-and-conquer algorithm used to cluster the gene expression data. ...
(the entire genetics sequences encoded in the DNA and responsible for the hereditary information). ...
doi:10.5772/48150
fatcat:r5qjaow3hreuplbwm5po4cn52y
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