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The values represent log2 data of fold changes between stressed and control samples.
We first log2-transformed the raw data of fold changes and then plotted the transformed data.
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These data consist of fold change values, differently from other studies that have used abundance (molecules/cell) [ 36] to study the correlation between protein and mRNA and the co-variables that affect such correlation [ 15, 37].
The scaling parameters for each attribute were calculated from the training data of each fold, and same scaling factors are applied to both corresponding training and testing data.
This analysed data comprised of fold-regulations, which represents the normalized gene expression in the TamR cells compared against the normalized gene expression in the MCF7 cells.
The accuracy of the microarray analysis was confirmed by real-time RT PCR analysis of the expression of six randomly selected differentially expressed genes, as the results showed good concordance with the microarray data in terms of fold change of gene expression (data not shown).
In average, the fold changes in RT-PCR data were approximately three times higher than that of array data indicating amplification of fold changes by sensitive real-time RT-PCR analysis.
Re-calculation of these data to fold-change revealed a somewhat higher up-regulation of the gene expression of TNFAIP3, NFKBIA, TRIM30 in our hands (Mathur vs. own data: TNFAIP3: 2.6 vs. 3.0; NFkBIA: 2.2 vs. 3.7; TRIM30: 3.8 vs. 5.04).
All experiments have complete data, except for one experiment [ 19] that has partial data at the level of fold change, due to the unavailability of raw data (.CEL) or signal intensity data.
Identification of control samples in an experiment facilitated incorporation of data at the level of fold change in RETINOBASE.
We validated our method by analyzing simulated extension trajectories that mimicked experimental data of single protein folding from optical tweezers.
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