Mining a massive RNA-seq dataset with biclustering: are evolutionary approaches ready for big data?

Finding meaningful structures in big data is challenging, especially within big and noisy data. In this short paper, we present the results of the application of 6 different biclustering methods to a massive human RNA-seq dataset with over 35k genes from over 125k samples. We assess which biclustering methods can handle that large data and compare the results to the mini-batch k-means, a popular clustering approach. Finally, we assess the importance of evolutionary-based approaches in biclustering 'big data'.

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