Biomedical subjects

Christopher Tong

Publications and source records attributed to Christopher Tong.

3 recordsLinked to original sources

PubMed2002

Energy-conserving low-order models for three-dimensional Rayleigh-Bénard convection.

Constructing hydrodynamic low-order models in the form of coupled gyrostats eliminates the possibility of certain unphysical behaviors, such as solutions diverging to infinity, that often appear in models resulting from ad hoc truncations of Galerkin approximations. In this paper, a simple low-order model in a gyrostatic form that conserves energy in the dissipationless limit (Model I) is constructed for three-dimensional (3D) Rayleigh-Bénard convection. It can be considered an energy-conserving extension of the model by Das et al. [Phys. Rev. E 62, R3051 (2000)] (Model II) that does not conserve energy and possesses solutions diverging to infinity. Also studied here is a smaller but energy-conserving subsystem of Model I that has the form of two coupled gyrostats (Model III). This new system is the 3D analog of the celebrated Lorenz model [J. Atmos. Sci. 20, 130 (1963)]. Stability diagrams and heat transport behavior are calculated and compared for the three models. Model I has improved qualitative agreement with experimental observations compared to that of Model II and Model III.

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Random forest: a classification and regression tool for compound classification and QSAR modeling.

A new classification and regression tool, Random Forest, is introduced and investigated for predicting a compound's quantitative or categorical biological activity based on a quantitative description of the compound's molecular structure. Random Forest is an ensemble of unpruned classification or regression trees created by using bootstrap samples of the training data and random feature selection in tree induction. Prediction is made by aggregating (majority vote or averaging) the predictions of the ensemble. We built predictive models for six cheminformatics data sets. Our analysis demonstrates that Random Forest is a powerful tool capable of delivering performance that is among the most accurate methods to date. We also present three additional features of Random Forest: built-in performance assessment, a measure of relative importance of descriptors, and a measure of compound similarity that is weighted by the relative importance of descriptors. It is the combination of relatively high prediction accuracy and its collection of desired features that makes Random Forest uniquely suited for modeling in cheminformatics.

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PubMedDate not supplied

Boosting: an ensemble learning tool for compound classification and QSAR modeling.

A classification and regression tool, J. H. Friedman's Stochastic Gradient Boosting (SGB), is applied to predicting a compound's quantitative or categorical biological activity based on a quantitative description of the compound's molecular structure. Stochastic Gradient Boosting is a procedure for building a sequence of models, for instance regression trees (as in this paper), whose outputs are combined to form a predicted quantity, either an estimate of the biological activity, or a class label to which a molecule belongs. In particular, the SGB procedure builds a model in a stage-wise manner by fitting each tree to the gradient of a loss function: e.g., squared error for regression and binomial log-likelihood for classification. The values of the gradient are computed for each sample in the training set, but only a random sample of these gradients is used at each stage. (Friedman showed that the well-known boosting algorithm, AdaBoost of Freund and Schapire, could be considered as a particular case of SGB.) The SGB method is used to analyze 10 cheminformatics data sets, most of which are publicly available. The results show that SGB's performance is comparable to that of Random Forest, another ensemble learning method, and are generally competitive with or superior to those of other QSAR methods. The use of SGB's variable importance with partial dependence plots for model interpretation is also illustrated.

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