Gene expression measurements represent the most important source of biological data used to unveil the interaction and functionality of genes. In this regard, several data mining and machine learning algorithms have been proposed that require, in a number of cases, some kind of data discretization to perform the inference. Selection of an appropriate discretization process has a major impact on the design and outcome of the inference algorithms, as there are a number of relevant issues that need to be considered. This study presents a revision of the current state-of-the-art discretization techniques, together with the key subjects that need to be considered when designing or selecting a discretization approach for gene expression data.
Anemopsis californica (Saururaceae) commonly called yerba mansa, is an important medicinal plant in many deserts in the southwestern region of North America. Populations of A. californica, collected throughout New Mexico, were examined for chemical variability in roots and rhizomes for select monocyclic (cymene, limonene, piperitone and thymol) and bicyclic (α-pinene, 1,8-cineole and myrtenol) monoterpenoid and phenylpropanoid (methyleugenol, isoeugenol and elemicin) derived essential oil components. Three distinct chemotypes were detected using a hierarchical clustering analysis on the concentration of 10 different analytes in three individuals from each of 17 populations. One chemotype was characterized by high elemicin concentrations, a second chemotype by high methyleugenol concentrations and the third by high piperitone and thymol concentrations. Steam distilled oil was used to screen for anticancer bioactivity. A. californica root oils demonstrated antiproliferative activity against AN3CA and HeLa cells in vitro but no activity against lung, breast, prostate or colon cancer cells. The IC 50 values for the root oil were 0.056% and 0.052% (v/v) for the AN3CA and HeLa cells respectively.
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