(C ) Observed distribution of IMDs in the AID* dataset compared to a simulation assuming mutations are randomly scattered throughout the genome.
Therefore, we designed a randomization algorithm for the Motif Mapper software [ 37] which enabled statistical evaluation of motif enrichment in a specific promoter dataset compared to a randomly composed dataset.
To interrogate if there are replication timing domains that are conserved across cancer types, we identified regions that displayed consistent differences in replication timing domains in the cancer datasets compared to all other non-cancer datasets.
These programs identify over-represented sequence patterns or motifs in a given dataset compared to all other datasets.
The novelty of our approach regarding the PPI data integration from major literature-curated source PPI datasets compared to existing meta-databases was the a priori definition of the set of nodes of the human protein interactome considering the UniProtKB manually reviewed human "complete" proteome as a robust, well-defined reference set.
To study the variable distributions in the derivation datasets compared to the validation dataset, statistical tests are used to identify the closeness of the variable distribution in each derivation dataset compared to the validation dataset.
The experimental results demonstrate the ability of the method to identify possible segmentations in a dataset, compared to algorithms that only yield a single clustering solution.
For example, to select a node with 5% of predictors in the corresponding dendrograms, a cutoff threshold above 0.6 (in the dissimilarity range 0.0 1.0) was required for Leukemia, SRBCT, and Alzheimer datasets, as compared to a lower threshold of 0.4 for the Colon dataset.
The meta-assemblies for each of the four datasets were compared to a single 25 k-mer length assembly.> We used multiple metrics to compare transcription quality between the 25 k-mer length assembly and the meta-assembly including: number of base pairs assembled, number of contigs, percent of reads used in the contigs, and median contig length.
Accuracy refers to the correctness of the dataset when compared to a reference dataset.
