Rec-I-DCM3: A Fast Algorithmic Technique for reconstructing large phylogenetic trees, Proceedings of the IEEE Computational Systems Bioinformatics Conference (ICSB)] to divide datasets into smaller subproblems.
The proposed DEFES is applied for forecasting the next day's closing price and we examined 2, 3 and 4 sub-populations and best results obtained by dividing dataset into two clusters that is shown in Table 3.
To implement this strategy we divided datasets of promoters to subsets.
Dividing datasets into acceptable and prolonged intervals would enable us to examine the experiences of patients with prolonged intervals in greater detail.
To better understand how codons are involved with this relationship, we divided datasets of As, Hs, and Es into two sub-datasets (light and heavy) according to their mean MWs.
Then, we divided datasets into subsets with biologically meaningful sample content (e.g. tissue, gender or disease state subsets), analyzed co-expression with BDNF across samples separately in each subset and confirmed the links across subsets.
We therefore divided datasets into sequencing bins by using thresholds of 1 million, 5 million, 12 million, and 24 million uniquely mapped reads (taking into account sequencing depths recommended in the past by the ENCODE consortium for TFs).
In addition, we introduced a new approach to increase the accuracy of the analysis: we divided datasets into subsets and sought for correlated genes for each subset, implying that each subset represents an independent experimental condition.
As we mentioned in dataset subsection, we divide the dataset into training set and test set.
However, as we performed duplicate measurements for each sample, it was possible to divide the dataset into 2 identical biological datasets B1 and B2 (see Additional file 4).
We divide the dataset to two parts.
