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Five different types of biologically relevant molecular datasets have been considered in this study.
Overall, molecular datasets have shown overwhelmingly strong support for placement of dibamids and gekkonids at the base of the tree, amphisbaenians with lacertoids, and iguanians with snakes and anguimorphs [ 17- 20, 23].
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The synergistic effect of the addition of morphological data to molecular datasets has been demonstrated before [ 63].
Analysis of this rapidly expanding molecular dataset has provided a robust and essentially independent test of the theories of evolutionary relationships previously derived from anatomical and developmental data.
A holistic, polyphasic approach, which integrates bioprocess, physiological and molecular biological datasets has been critical to the development of the LTAD concept.
Incongruence between phylogenies derived from morphological versus molecular analyses, and between trees based on different subsets of molecular sequences has become pervasive as datasets have expanded rapidly in both characters and species.
Because the studied datasets have almost the same molecular weight distribution and the same number of molecules, the influence of molecular weight on structural analysis can be effectively removed.
Comparison study showed that the three studied datasets have different distributions of molecular weight [27].
Having only been explored for confirming predicted miRNA targets [ 24, 25] or extracting tumor-classifying molecular signatures [ 26], these parallel expression datasets have far more potential to be exploited.
Although independent groups and datasets have confirmed the involvement of molecular and genetic factors in the progression of DCIS, none can currently be considered robust enough to be used as molecular markers for risk stratification in patients with DCIS.
In summary, evolutionary analyses from high throughput functional genomic datasets have formulated the genome factor hypothesis of molecular evolution.
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