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In this report we have evaluated metabolite and RNA profiling technologies to begin to understand the natural variation in these biomolecules found in commercial-quality, conventional (non-GM) maize hybrids. Our analyses focus on mature grain, the article of commerce that is most typically subjected to the rigorous studies involved in the comparative safety assessment of GM products. We have used a population of conventionally-bred maize hybrids that derive from closely related inbred parents grown under standard field conditions across geographically similar locations. This study highlights the large amount of natural variation in metabolites and transcripts across conventional maize germplasm grown under normal field conditions, and underscores the critical need for further extensive studies before these technologies can be seriously considered for utility in the comparative safety assessment of GM crops.
Our entire medical framework is based on the concept of disease, understood as a qualitative departure from normality (health) with a structural substrate (lesion), and usually an identifiable cause (aetiology). This paradigm is loaded with problems, some of which are discussed in the text. Nevertheless, we study, diagnose and treat diseases, and while often painfully conscious of the dysfunctionalities of this scheme, we can hardly imagine how we could practice medicine otherwise. However, most of the recent developments in basic sciences, and most notably in Immunology, Genetics and -omics, are inconsistent with this “health/disease” paradigm. The emerging scenario is that of complex networks, more in the spirit of Systems Biology. In these settings the qualitative difference between health and disease loses its meaning, and the whole discourse becomes progressively irreducible to our conventional clinical categories. As clinical research stagnates while basic sciences thrive, this gap is widening, and a change in the prevailing paradigm seems unavoidable. However, all our clinical judgments (including Bayesian reasoning and Evidence Based Medicine) are rooted in the disease/health dichotomy, and one can hardly conceive how they could work without it. The shift in paradigm will not be easy, and certain turmoil is to be expected.
Omics data provides an essential means for molecular
biology and systems biology to capture the systematic properties of inner
activities of cells. And one of the strongest challenge problems biological
researchers have faced is to find the methods for discovering biomarkers for
tracking the process of disease such as cancer. So some feature selection methods
have been widely used to cope with discovering biomarkers problem. However
omics data usually contains a large number of features, but a small number of
samples and some omics data have a large range distribution, which make feature
selection methods remains difficult to deal with omics data. In order to
overcome the problems, wepresent a
computing method called localized statistic of abundance distribution based on
Gaussian window(LSADBGW) to test the significance of the feature. The
experiments on three datasets including gene and protein datasets showed the
accuracy and efficiency of LSADBGW for feature selection.