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By Luh W., Wiessner M.

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30 3. 1. A. Sboner et al. METHODS OF ANALYSIS Detection Methods Following [13], we considered the “QC_dataset” as the matrix depicted in Table 1. We label this matrix E, denoting with E(o, t) an element of E. The variable o indexes the oligos from Oligo1 to Oligo5080 , and for the variable t, t ∈ TP, where TP = {TP1 , . . , TP22 , TP24 , . . , TP28 , TP30 , . . , TP48 }. TP23 and TP29 were not provided by [2,4]. 01) [11,2]. The local weighting parameter was reduced to 12%. In order to find within the E matrix gene expressions with rapid changes in time (in particular, candidate peaks and valleys), we exploited six different methods (labeled Mi , i = 1, .

As our program creates PGN networks, a negative control was idealized to further validate the biological value of our findings. Thus, eight genes, four from glycolysis and four from the apicoplast organelle, were chosen randomly and used together as seed genes to create PGN networks based on single-gene and two-gene predictions. html). html). It is important to recall that two-gene predictions are based on 21,330,246 calculations further reinforcing the value of these results. Together, this data demonstrates the value of the PGN model in generating biologically meaningful networks and which include genes not included by the Fourier approach [1].

Azzinia , E. Blanzierib and R. Dell’Annaa a Bioinformatics Group, SRA Division, ITC-irst, Via Sommarive 18, I-38050 Povo (TN), Italy b Department of Information and Communication Technology, University of Trento, Via Sommarive 14, I-38050 Povo (TN), Italy Abstract Given a set of gene expression time series obtained by a microarray experiment, this work proposes a novel quality control procedure that exploits six analytical methods, each of which allows for the identification in an automated way of genes that have expression spikes within narrow time-windows and over a chosen amplitude threshold.

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