Examination of the TCGA dataset revealed that CBX6 is not differentially expressed in different subtypes of breast cancer (Supplementary Fig. S1A).
Additionally, the results for training and testing the dataset revealed that the hybrid method enhances RBF network learning more efficiently in comparison with other conventional RBF approaches.
Interrogation of this dataset revealed that smarca2, an ATP-dependent chromatin remodeler, was among the five genes that showed the most significant elevation in transcript levels after miR-430 processing was blocked.
After compensation for sample loss caused by expected acoustic interference between the transmitter tags, the resulting dataset revealed that the receiver units collected 90 95% of the signals in both cages.
Examination of the compounds in the dataset revealed that a small number of cases had disconnected components.
For example, a simple analysis of representative happiness and sadness words in the MC dataset revealed that while these words were on average voted by 92.1% and 87.9% of the participants as happiness and sadness, respectively, only 87.1% and 78.4 % of the participants voted these words as positive and negative, respectively.
Further investigation of the lysine molecule dataset revealed that a subset of structures that had been generated at a lower level of theory in the initial stages of the structure-search procedure made their way by mistake into the final dataset.
A closer look at this dataset revealed that it consisted of: 4,658 ligands annotated over 31 enzymes, Table 1; 5,031 ligands distributed over 23 membrane receptors, Table 2; and 1,149 ligands annotated over four transporters, one ion-channel and one transcription factor, Table 3.
Bayesian analyses of the combined dataset revealed that topologies were very similar or identical to those obtained using maximum parsimony.
A similar analysis of equine influenza signatures, conducted using the EQ dataset, revealed that they have predominantly avian signatures (Figure 9).
Interestingly, KEGG analysis of our selected dataset, revealed that the most significant pathway was that of Bladder Cancer (p = 1.5×10-31).
