Experiments on synthetic and microarray data suggest that advantages of the proposed probabilistic GO term inference over statistical test-based approaches are in particular evident for sparsely annotated GO terms and in situations of large uncertainty about gene activity.
In support of this flexibility, an effective adaptation of this approach is demonstrated which performs real-time analysis of extracted microarray data; the proposed reconfigurable multi-core prototype has been exploited with minor changes to achieve almost 5× speedup.
To exploit the information redundancy that exists among the huge number of variables and improve classification accuracy of microarray data, we propose a gene selection method, Recursive Feature Addition (RFA), which is based on supervised learning and similarity measures.
Having used multiple strategies and integrated the PM data with the in vitro and in vivo microarray data, we propose the following hypotheses about streptococcal niche adaptation and switch to hypervirulence: (1) Exposure to the in vivo environment perturbs a substantial number of GAS genes and regulatory networks.
In this study, a multiple criteria representation of the gene expression changes identification problem using microarray data is proposed.
This paper integrates dynamic population strategy and shuffled frog-leaping algorithm into biclustering of microarray data, and proposes a novel multi-objective dynamic population shuffled frog-leaping biclustering (MODPSFLB) algorithm to mine maximum bicluesters from microarray data.
Comparing with the previous models that only employ microarray data, the proposed model can bridge the gap between the relative background frequency of the observed nucleotide and the gene's transcription rate.
To adapt FASTCORE for the direct integration of microarray data, we propose here a new workflow: FASTCORMICS pre-processes microarray data with the discretization tool Barcode [ 29, 30], is devoid of arbitrary parameter settings and has a low computational demand with overall context-specific model building times in the order of a few minutes.
First, in order to improve upon the limitations of microarray data, we now propose a method to combine microarray expression data with mRNA:microRNA binding information.
To address these challenges, different effect size measurements and models for integrating microarray data have been proposed.
