Hao-Hua Meng

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Activities of drug molecules can be predicted by QSAR (quantitative structure activity relationship) models, which overcome the disadvantage of high cost and long cycle by employing the traditional experimental method. With the fact that the number of drug molecules with positive activity is rather fewer than that with negatives, it is important to predict(More)
Multivariate calibration is a classic problem in the analytical chemistry field and frequently solved by Partial Least Squares and Artificial Neural Networks in the previous works. The spaciality of mul-tivariate calibration is high dimensionality with small sample. Here we apply Support Vector Regression (SVR) as well as Artificial Neural Networks , and(More)
BACKGROUND Since the high dimensionality of gene expression microarray data sets degrades the generalization performance of classifiers, feature selection, which selects relevant features and discards irrelevant and redundant features, has been widely used in the bioinformatics field. Multi-task learning is a novel technique to improve prediction accuracy(More)
In many cases, protein mass-spectrometry data are imbalanced, i.e. the number of positive examples is much less than that of negative ones, which generally degrade the performance of classifiers used for protein recognition. Despite its importance, few works have been conducted to handle this problem. In this paper, we present a new method that utilizes the(More)
Most of microarray data sets are imbalanced, i.e. the number of positive examples is much less than that of negative, which will hurt performance of classifiers when it is used for tumor classification. Though it is critical, few previous works paid attention to this problem. Here we propose embedded gene selection with two algorithms i.e. EGSEE (Embedded(More)
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