Publications

@article{Lopez-Fernandez2018,

title = {BIGO: A web application to analyse gene enrichment analysis results},

author = {A. Lopez-Fernandez and D. Rodríguez-Baena and F. Gómez-Vela and N. Díaz-Díaz},

url = {http://www.sciencedirect.com/science/article/pii/S147692711730823X},

doi = {10.1016/j.compbiolchem.2018.06.006},

issn = {1476-9271},

year  = {2018},

date = {2018-05-01},

journal = {Computational biology and chemistry},

volume = {76},

pages = {169-178},

abstract = {Background and objective

Gene enrichment tools enable the analysis of the relationships between genes with biological annotations stored in biological databases. The results obtained by these tools are usually difficult to analyse. Therefore, researchers require new tools with friendly user interfaces available on all types of devices and new methods to make the analysis of the results easier.



Methods

In this work, we present the BIGO Web tool. BIGO is a friendly Web tool to perform enrichment analyses of a collection of gene sets. On the basis of the obtained enrichment analysis results, BIGO combines the biological terms to organize them and graphically represents the relationships between gene sets to make the interpretations of the results easier.



Results

BIGO offers useful services that provide the opportunity to focus on a concrete subset of results by discarding too general biological terms or to obtain useful knowledge by means of the visual analysis of the functional connections between the sets of genes being analysed.



Conclusions

BIGO is a web tool with a novel and modern design that provides the possibility to improve the analysis tasks applied to gene enrichment results.},

keywords = {Bioinformatics, Biological validation, Gene enrichment analysis},

pubstate = {published},

tppubtype = {article}

}

@article{Rodríguez-Baena2013,

title = {Extracting and validating biclusters from binary datasets},

author = {D. Rodríguez-Baena},

doi = {10.3233/AIC-130570},

year  = {2013},

date = {2013-05-01},

journal = {AI Communications},

volume = {26},

number = {4},

pages = {417-418},

abstract = {This work proposes a novel algorithm to extract biclusters from binary datasets: the Bit-Pattern Biclustering Algorithm (BiBit). The selective search performed by BiBit, based on a very fast bits words processing technique, provides very satisfactory results in quality and computational cost. Besides, a new software tool, named CarGene (Characterization of Genes), that helps scientists to validate sets of genes using biological knowledge is introduced too.},

keywords = {Biclustering, Binary dataset, Biological validation},

pubstate = {published},

tppubtype = {article}

}

@article{Díaz-Díaz2013,

title = {Genes functional coherence based on actual biological knowledge},

author = {N. Díaz-Díaz},

url = {https://content.iospress.com/articles/ai-communications/aic553},

doi = {10.3233/AIC-130553},

year  = {2013},

date = {2013-01-02},

journal = {AI Communications},

volume = {26},

number = {2},

pages = {247-249},

abstract = {This work proposes two new approaches to establish the quality of genetic model based on current biological knowledge. First, it is developed a KEGG-based tool that provides a friendly graphical environment to analyze gene-enrichment. Moreover, a novel GO-based dissimilarity measure is proposed for evaluating groups of genes based on the most relevant functions of the whole set. To found this function, an heuristic approach based on Voronoi diagram has been presented.},

keywords = {Biological validation, Gene enrichment analysis},

pubstate = {published},

tppubtype = {article}

}

@article{Díaz-Díaz2011c,

title = {GO-based Functional Dissimilarity of Gene Sets},

author = {N. Díaz-Díaz and J. Aguilar-Ruiz},

doi = {10.1186/1471-2105-12-360},

year  = {2011},

date = {2011-09-01},

journal = {BMC Bioinformatics},

volume = {12},

number = {360},

abstract = {Background

The Gene Ontology (GO) provides a controlled vocabulary for describing the functions of genes and can be used to evaluate the functional coherence of gene sets. Many functional coherence measures consider each pair of gene functions in a set and produce an output based on all pairwise distances. A single gene can encode multiple proteins that may differ in function. For each functionality, other proteins that exhibit the same activity may also participate. Therefore, an identification of the most common function for all of the genes involved in a biological process is important in evaluating the functional similarity of groups of genes and a quantification of functional coherence can helps to clarify the role of a group of genes working together.



Results

To implement this approach to functional assessment, we present GFD (GO-based Functional Dissimilarity), a novel dissimilarity measure for evaluating groups of genes based on the most relevant functions of the whole set. The measure assigns a numerical value to the gene set for each of the three GO sub-ontologies.



Conclusions

Results show that GFD performs robustly when applied to gene set of known functionality (extracted from KEGG). It performs particularly well on randomly generated gene sets. An ROC analysis reveals that the performance of GFD in evaluating the functional dissimilarity of gene sets is very satisfactory. A comparative analysis against other functional measures, such as GS2 and those presented by Resnik and Wang, also demonstrates the robustness of GFD.},

keywords = {Biological validation},

pubstate = {published},

tppubtype = {article}

}