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By use of phased single-nucleotide polymorphism data, our method partitions a chromosome into a series of adjacent, nonoverlapping blocks.
In extensive experiments with simulated and real data, our method was shown to outperform existing tools in terms of accuracy of characterizing phenotypes using DEGs.
To ascertain that the SAR model accounts for all the spatial autocorrelation present in the data, our method requires to perform again the Moran's test.
When applied to yeast ChIP-chip data, our method reveals that only 48% of the data sets can be readily explained by direct binding of the profiled TF, while 16% can be explained by indirect DNA binding.
We believe that with the explosion of available human PPI data, our method will contribute greatly to the functional research of human proteins.
On real data, our method detects a higher number of nucleosomes.
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On simulated data, our methods performed significantly better than existing methods across a wide range of contrast-to-noise ratios and feature prevalence rates.
On experimental fMRI data, our methods were more effective in selectively isolating a distributed fronto-temporal network that distinguished between brain regions known to be involved in speech and music processing.
In this paper, we describe the data, our methods for transformation to SBPkb, and finally, we demonstrate the value of our knowledgebase with a set of sample queries.
For the analysis of specific omics data, our methods (and software) might require some modifications.
Differently from previous methods based on PPI data, our methods combine the domain information of protein itself and take protein complex information for consideration.
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