Confidence bands and hypothesis tests for hit enrichment curves.

In virtual screening for drug discovery, hit enrichment curves are widely used to assess the performance of ranking algorithms with regard to their ability to identify early enrichment. Unfortunately, researchers almost never consider the uncertainty associated with estimating such curves before declaring differences between performance of competing algorithms. Uncertainty is often large because the testing fractions of interest to researchers are small.

chemmodlab: a cheminformatics modeling laboratory R package for fitting and assessing machine learning models.

The goal of chemmodlab is to streamline the fitting and assessment pipeline for many machine learning models in R, making it easy for researchers to compare the utility of these models. While focused on implementing methods for model fitting and assessment that have been accepted by experts in the cheminformatics field, all of the methods in chemmodlab have broad utility for the machine learning community. chemmodlab contains several assessment utilities, including a plotting function that constructs accumulation curves and a function that computes many performance measures.

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Permeation of chemical solutes through skin can create major health issues. Using the membrane-coated fiber (MCF) as a solid phase membrane extraction (SPME) approach to simulate skin permeation, we obtained partition coefficients for 37 solutes under 90 treatment combinations that could broadly represent formulations that could be associated with occupational skin exposure. These formulations were designed to mimic fluids in the metalworking process, and they are defined in this manuscript using: one of mineral oil, polyethylene glycol-200, soluble oil, synthetic oil, or semi-synthetic oil; at a concentration of 0.

Analysis of High-Dimensional Structure-Activity Screening Datasets Using the Optimal Bit String Tree.

A new classification method called the Optimal Bit String Tree is proposed to identify quantitative structure-activity relationships (QSARs). The method introduces the concept of a to describe the presence/absence context of a combination of descriptors. A descriptor set and its optimal chromosome form the splitting variable.

ChemModLab: a web-based cheminformatics modeling laboratory.

ChemModLab, written by the ECCR @ NCSU consortium under NIH support, is a toolbox for fitting and assessing quantitative structure-activity relationships (QSARs). Its elements are: a cheminformatic front end used to supply molecular descriptors for use in modeling; a set of methods for fitting models; and methods for validating the resulting model. Compounds may be input as structures from which standard descriptors will be calculated using the freely available cheminformatic front end PowerMV; PowerMV also supports compound visualization.

A model-based ensembling approach for developing QSARs.

Ensemble methods have become popular for QSAR modeling, but most studies have assumed balanced data, consisting of approximately equal numbers of active and inactive compounds. Cheminformatics data are often far from being balanced. We extend the application of ensemble methods to include cases of imbalance of class membership and to more adequately assess model output.

An autoregressive point source model for spatial processes.

We suggest a parametric modeling approach for nonstationary spatial processes driven by point sources. Baseline near-stationarity, which may be reasonable in the absence of a point source, is modeled using a conditional autoregressive (CAR) Markov random field. Variability due to the point source is captured by our proposed autoregressive point source (ARPS) model.

Domain-enhanced analysis of microarray data using GO annotations.

Motivation: New biological systems technologies give scientists the ability to measure thousands of bio-molecules including genes, proteins, lipids and metabolites. We use domain knowledge, e.g.

A factorial design to optimize cell-based drug discovery analysis.

Drug discovery is dependent on finding a very small number of biologically active or potent compounds among millions of compounds stored in chemical collections. Quantitative structure-activity relationships suggest that potency of a compound is highly related to that compound's chemical makeup or structure. To improve the efficiency of cell-based analysis methods for high throughput screening, where information of a compound's structure is used to predict potency, we consider a number of potentially influential factors in the cell-based approach.