86 research outputs found
Evaluation of machine-learning methods for ligand-based virtual screening
Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier (NBC) methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of an NBC when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed
Indirect costs of non-healing diabetic foot wounds in an African origin population in Barbados
Direct healthcare services cost of non-healing diabetic foot wounds in an African origin population in Barbados
Credit Expansion and NPLs: A Panel Data Analysis for Western Europe and the United States
Vascular Occlusion in Cut Flowering Rose Stems Exposedto Air: Role of Water Entry into the Lumina of the Xylem Conduits Opened by Cutting
- …