LCS was shown by [ 11] to be superior for identifying related pathways in different databases, compared to approaches based on large overlap of genes and interacting gene pairs.
In this work, we investigated the functional relation between miRNA host genes and putative targets of corresponding intronic miRNAs with a data-driven approach based on large-scale gene expression data and a knowledge-based approach using gene annotations.
Breast cancer classification has recently evolved with the definition of molecular subtypes with different prognosis based on large-scale gene expression profiling [ 7, 41] and protein expression profiling [ 42, 43].
While this may indeed have consequences for individual genes, the impact on our functional analysis, which is based on large numbers of genes, will be minimal.
This is because these analyses were based on large numbers of genes representing a range of recombination rates.
Phylogenetic analyses based on large data sets of mitochondrial gene sequences (4669 bp) obtained from the East Asian Carassius auratus-complex revealed the existence of two superlineages, one distributed mainly in the Japanese main islands and the other in various regions in and around the Eurasian continent.
Other analyses based on large data sets feature commonly associated genes, but they also include a large proportion of 'unexpected markers'.
Four target genes were selected based on large upregulation (≈10 – 200-fold changes) and consistent differential expression in most of the vegetative stage comparisons or possible roles in stress response, including ones previously identified in soybean leaf tissues [ 15].
The parametric linkage approach is very effective for locating single-gene disorders and is usually based on large family pedigrees.
In a study of Subramanian et al., the neighborhoods highly correlated with these cancer-associated genes were selected based on four large gene expression datasets that were collected from various cancer projects mainly on primary tumors, including prostate, breast, lung, lymphoma, and leukemia [ 9].
