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Poster presentation at 5th German Conference on Cheminformatics: 23. CIC-Workshop Goslar, Germany. 8-10 November 2009 We demonstrate the theoretical and practical application of modern kernel-based machine learning methods to ligand-based virtual screening by successful prospective screening for novel agonists of the peroxisome proliferator-activated receptor gamma (PPARgamma) [1]. PPARgamma is a nuclear receptor involved in lipid and glucose metabolism, and related to type-2 diabetes and dyslipidemia. Applied methods included a graph kernel designed for molecular similarity analysis [2], kernel principle component analysis [3], multiple kernel learning [4], and, Gaussian process regression [5]. In the machine learning approach to ligand-based virtual screening, one uses the similarity principle [6] to identify potentially active compounds based on their similarity to known reference ligands. Kernel-based machine learning [7] uses the "kernel trick", a systematic approach to the derivation of non-linear versions of linear algorithms like separating hyperplanes and regression. Prerequisites for kernel learning are similarity measures with the mathematical property of positive semidefiniteness (kernels). The iterative similarity optimal assignment graph kernel (ISOAK) [2] is defined directly on the annotated structure graph, and was designed specifically for the comparison of small molecules. In our virtual screening study, its use improved results, e.g., in principle component analysis-based visualization and Gaussian process regression. Following a thorough retrospective validation using a data set of 176 published PPARgamma agonists [8], we screened a vendor library for novel agonists. Subsequent testing of 15 compounds in a cell-based transactivation assay [9] yielded four active compounds. The most interesting hit, a natural product derivative with cyclobutane scaffold, is a full selective PPARgamma agonist (EC50 = 10 ± 0.2 microM, inactive on PPARalpha and PPARbeta/delta at 10 microM). We demonstrate how the interplay of several modern kernel-based machine learning approaches can successfully improve ligand-based virtual screening results.
Les recherches ethnologiques effectuées jusqu'à ce jour se sont concentrées principalement sur deux grands axes. Elles ont d'une part dressé un inventaire détaillé des forges et de leurs techniques dans les différents groupes ethniques de cette région. D'autre part, elles ont examiné l'histoire des migrations et de la sédentarisation des forgerons. Il serait intéressant de savoir si les résultats des recherches linguistiques concordent avec ceux des travaux d'ethnologie. Jusqu'ici, les recherches ont montré la mobilité des forgerons professionnels. Ces "travailleurs transfrontaliers" de la culture jouent le rôle de médiateurs et favorisent les échanges culturels à l'intérieur de chaque ethnie et entre les différentes ethnies. En raison de cette mobilité, l'inventaire et les produits des forges présentent souvent des caractères identiques, même sur des espaces géographiques de plus grande taille. Par contre, le vocabulaire est souvent influencé par des mots empruntés à d'autres langues. Ce phénomène constituera précisément un des éléments centraux dans la suite de nos recherches sur le thème des artisans.
Non-standard errors
(2021)
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.