However, TopoGSA enables the user to compare the properties of a dataset of interest with a multitude of predefined datasets corresponding to known functional processes from public databases.
The choice of a concrete machine learning classifier depends on a multitude of parameters, e.g., dataset size, feature space dimensionality, available computational resources, robustness against adversarial attacks, etc., and classifiers are tailored for different uses.
Due to the advent of high through-put technologies, a multitude of public domain expression datasets are now available for CRC research.
Soon thereafter, a suite of virtual observatories (VxO) started to emerge, with the task of registering a multitude of new data providers and their datasets in a uniform fashion under the framework of the SPASE metadata model (King et al., 2008).
A detailed analysis of 20 strand-specific RNA-Seq datasets revealed a multitude of transcriptionally active genomic regions.
Note, however, that apart from true miRNAs and the above-mentioned sequence variants, the dataset contains a multitude of sequences not related to miRNAs, such as other non-coding RNAs and mRNA degradation products.
Best results were achieved by datasets which use a multitude of input data, namely infrared and microwave satellite data, as well as observations from rain gauges (usually GPCC) for bias correction.
Integrating these approaches into a single workflow provides complementary datasets that report a multitude of compositional lipid features ranging from overall FA levels to quantification of individual molecular lipid species.
In mice, several differential gene expression studies involving a multitude of traits have been conducted, resulting in large datasets which are publicly available [ 17, 18].
Unfortunately the availability of ethnicity data in both datasets has not been improving as rapidly as hoped for, due to a multitude of factors e.g. people are reluctant to collect data they do not feel is being utilised.
