Gene expression pattern data has been used in the past to uncover the function of a gene.
The patterns in the data have made Professor Wolfers believe that these markets — where traders effectively bet on the outcome of real-world events — are a better guide to the future than experts are.
Because of the tremendous diversity and complexity of cancer gene mutations, cancers even the same type of cancers show very different mutation profiles and few common patterns behind these data have been identified.
In this perspective, the Association Rule Mining (ARM) based approaches, looking for frequent patterns in the data, have been proposed as an alternative methodology to analyze expression data [ 15, 16].
3 4 Use of prescription medicine is one health-related outcome that has been found to be socially patterned, where Danish data have shown a greater use among people with low SEP compared with people with high SEP. 5 6 Denmark has a tax-financed decentralised healthcare system with a partly need-dependent reimbursement system for outpatient prescription drugs.
For the Drosophila gene expression pattern data, we have n gene expression images bags X={ x1,···, x n }, where each image bag is abstracted as a data point x i ∈ℝ p. Each data point x i belongs to one of K c stage classes ={ c1,···, c K c } represented by y i c ∈{0, 1} K c, such that y i c (k)=1 if x i is classified into class c k, and 0 otherwise.
A subset of Drosophila miRNAs are subject to length pattern control by Nbr; moreover, recent data have shown that the pattern may reflect differential loading into the miRISC and siRNA RISC complexes with age (Abe et al., 2014).
Estimated expression patterns from digital northern data have been found to be well matched with RT-PCR data [58].
The discovery of mutated genes in human cancers and the patterns behind the mutation data have provided critical insights into the mechanisms underlying cancer formation and development, and have proven helpful for cancer therapy [3], [12].
Classification methods, because of their power to unravel patterns in biologically complex data, have become one of the most important bioinformatics approaches investigated for use with omics data.
