Due to the availability of more and more large-scale datasets from modern pyro-sequencing techniques, high-throughput analysis methods for thousands of RNAs are needed.
Such systematic explorations raise the possibility for targeted interventions and will become a growing trend in the future as more large-scale datasets like the CMap will become available.
More large-scale dataset with more complex notion of causality can also be incorporated into our methodology as they become available in the future.
The continuing decline in the cost of high throughput "Next Generation" sequencing methods ensures that many more large scale transcriptome datasets will become available in the near future for these and other genome-enabled approaches to plant phylogenetics.
Big data - large scale datasets of operational transactional data - could have massive potential for improving governance, in terms of making government bodies more efficient, more effective, less prone to failure (identifying failing services or inadequate contractors, for example) and more citizen focused, as well as aiding policy design and development.
However, the enlarging volume of datasets makes regression by ELM on very large scale datasets a challenging task.
However, GP regression is restrictive for large scale datasets due to its computation complexity.
Over the coming years, more and more large-scale omics datasets will become available to characterize patients with complex and heterogeneous diseases at a molecular level.
With wide applications of MALDI-TOF MS and SELDI-TOF MS in biomedical studies, more and more large-scale MS datasets have being obtained [ 1- 6].
As more and more large-scale microbiome datasets are being collected, we expect that our generalized UniFrac distances can help to identify important covariates that are associated with the microbiomes that could be missed using the commonly used UniFrac distances.
Comparison results show that ML-KELM outperformance in large scale dataset with high dimension instance feature.
