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We applied this strategy to the ChIP-seq data provided in the CAMDA (Critical Assessment of Massive Data Analysis) 2009 challenging data set [11], which includes ChIP-seq data on RPol II in HeLa S3 cells, and ChIP-seq data on a transcription factor STAT1 (signal transducers and activators of transcription-1) in the same cell line with interferon γ stimulation.
All this variations make a very challenging data set.
In their study [15], Caputo et al. proposed a challenging data set for material recognition called textures under varying illumination, pose and scale (KTH-TIPS) 2a and a methodology to evaluate it.
In the following, we will compare ANGLOR with the second (more challenging) data set.
In order to understand the role of the microRNA-mediate regulatory network in interferon γ – stimulated HeLa cells, we conducted bioinformatics analysis of the RPol II and STAT1 ChIP-seq data provided by the CAMDA 2009 challenging data set [11].
We applied this strategy to the 2009 CAMDA challenging data set [11], in which genome-wide binding patterns of RPol II and STAT1 were measured in HeLa S3 cells under control and interferon γ stimulated conditions, respectively.
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The calculated sensitivities were used on the challenge data set.
We therefore used the same threshold for the CASMI challenge data set.
Each software provided candidate ranking for each MS/MS spectrum from the training and challenge data set.
In the challenge data set, 208 MS/MS spectra were matched against the combined MassBank and NIST libraries.
MS offers accurate noise PSD estimation especially if the noise signal is stationary to a certain extent, i.e., varies slowly compared to the statistics of the desired speech component, which is true for the noise contained in the REVERB challenge data set.
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