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Another shortcoming evident is that too many associations are generated from such kind of data.
Let us mention that, for such kind of data, the gain in segmentation accuracy by using the proposed model is also restrained (τMN-HMC = 7.6% against τMS-HMC = 9.1%).
Treatment effects on HRQL and population values are commonly estimated using regression techniques, however, HRQL scores typically exhibit specific properties that make the use of ordinary least square (OLS) regression at least doubtful for such kind of data [ 3, 4].
Meta-analytic methodologies for such kind of data have been developed to summarize sensitivity and specificity separately or jointly in a fixed or random effects context, for example [ 1- 6].
However, in case strains of PGPM species utilised in the formulation are known to be potentially or opportunistic pathogens for mammals (e.g. Enterobacter, Pantoea, Klebsiella and Burkholderia), such kind of data shall be required to be provided and assessed before granting the authorisation for marketing (Frangolias et al. 1999; Guo et al. 2002).
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A common problem in such kind of data-driven learning approaches is that only a small number of genes can be modelled.
Such kinds of data are still rare.
Future research can be undertaken to establish factors that influence reliability and validity of such kinds of data.
For this reason, it was inappropriate to run any large-scale population genetic software like STRUCTURE [ 40] on the whole data set since the underlying model is not suited to such kinds of data.
Medical records in primary care in Sweden are generally regarded as a reliable source of such kinds of data collection since the PHC centres have an overall responsibility for the PHC in a catchment area, and therefore are required to regularly report morbidity patterns based on structured diagnosis.
However, such kind of training data is hard to collect in the real world.
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