Finally, this probabilistic algebraic model summarizes all the experimental data bases and consequently can be reused for other researches.
Some groups recently reported that over-representation of genes with higher expression in one of the samples, i.e., biased differential expression, has a negative impact on data normalization and consequently can lead to biased estimates of true differentially expressed genes (DEGs) [ 15, 16].
In particular, the available data facilitates investigation of the chromatin modification landscape in functionally unknown regions and consequently can provide a more comprehensive view of biological interactions.
MiRNA are known to regulate many target genes and consequently can modulate different signaling pathways.
But published studies practically never present these data, and, consequently, such tables can not be constructed.
However, this independent anonymization may limit the data fusion capabilities, thereby severely restricting the range of analyses that can be performed on the data and, consequently, the knowledge that can be generated from them.
14 15 Some tools include laboratory data but consequently can be applied only when such data are available.
Consequently, normalization of gene expression with a single reference gene can trigger erroneous data and, consequently, misinterpretation of experiment results.
The major advantage of DNNs is that they can discover some structure in the training data and consequently incrementally modify the data representation, resulting in a superior accuracy of trained networks.
On the flip side, true positive predictions can equally be refuted by fresh experimental data and consequently turn into a false positive.
