BIGO: A web application to analyse gene enrichment analysis results

Fernández, Aurelio López; Rodríguez–Baena, Domingo S.; Gómez-Vela, Francisco; Díaz–Díaz, Norberto

doi:10.1016/j.compbiolchem.2018.06.006

Cited by 1 publication

(1 citation statement)

References 37 publications

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“…The following tools, like gProfiler [30], Enrichr [31], UBiT2 [32], or WebGestalt [33], are some examples. The only exception is BiGO [34], that is able to analyze and compare more than one gene list at a time, although it has not been designed to scale against a very high number of results. The lack of validation methods to massively process a large number of biclusters makes some of the latest Biclustering approaches have to limit their results.…”

Section: Introductionmentioning

confidence: 99%

gMSR: A Multi-GPU Algorithm to Accelerate a Massive Validation of Biclusters

2020

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Nowadays, Biclustering is one of the most widely used machine learning techniques to discover local patterns in datasets from different areas such as energy consumption, marketing, social networks or bioinformatics, among them. Particularly in bioinformatics, Biclustering techniques have become extremely time-consuming, also being huge the number of results generated, due to the continuous increase in the size of the databases over the last few years. For this reason, validation techniques must be adapted to this new environment in order to help researchers focus their efforts on a specific subset of results in an efficient, fast and reliable way. The aforementioned situation may well be considered as Big Data context. In this sense, multiple machine learning techniques have been implemented by the application of Graphic Processing Units (GPU) technology and CUDA architecture to accelerate the processing of large databases. However, as far as we know, this technology has not yet been applied to any bicluster validation technique. In this work, a multi-GPU version of one of the most used bicluster validation measure, Mean Squared Residue (MSR), is presented. It takes advantage of all the hardware and memory resources offered by GPU devices. Because of to this, gMSR is able to validate a massive number of biclusters in any Biclustering-based study within a Big Data context.

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