CYP2C19 is an important human drug-metabolizing enzyme that metabolizes a number of clinically used drugs including the antiulcer drug omeprazole, the anxiolytic drug diazepam, the beta-blocker propranolol, the antimalarial drug proguanil, certain antidepressants and barbiturates, and the prototype substrate S-mephenytoin. Previous studies show that compounds such as rifampicin and dexamethason...

The commonly used two-sample tests of equal area-under-the-curve (AUC), where AUC is based on the linear trapezoidal rule, may have poor properties when observations are missing, even if they are missing completely at random (MCAR). We propose two tests: one that has good properties when data are MCAR and another that has good properties when the data are missing at random (MAR), provided that ...

We consider maximum likelihood (ML) estimation of mean and covariance structure models when data are missing. Expectation maximization (EM), generalized expectation maximization (GEM), Fletcher-Powell, and Fisherscoring algorithms are described for parameter estimation. It is shown how the machinery within a software that handles the complete data problem can be utilized to implement each algor...

Eighty-five specimens of breast tissue were grouped and investigated by the technic of mixed cell agglutination reation (MCAR) for studying isoantigens A, B and H (0). MCAR was found to be strongly positive at the epithelium of acini in all the 25 subjects with benign lesions (10 fibroadenomas, 10 cystic mastitis and 5 gynecomastias). On the other hand, all the subjects with diagnosis of primar...

ÐMissing data are often encountered in data sets used to construct effort prediction models. Thus far, the common practice has been to ignore observations with missing data. This may result in biased prediction models. In this paper, we evaluate four missing data techniques (MDTs) in the context of software cost modeling: listwise deletion (LD), mean imputation (MI), similar response pattern im...

A Kernel-Based Nonparametric Multiple imputation method is proposed under MAR (Missing at Random) and MCAR (Missing Completely at Random) missing mechanisms in nonparametric regression settings. We experimentally evaluate our approach, and demonstrate that our imputation performs better than the well-known NORM algorithm.

Imputing values to missing cases is a subject that frequently met in the fields of Machine Learning and Data Mining, require researchers study it. It known many computer-based analysis algorithms operate under assumption there no case. The lack sufficient search case by able negatively affect performance results. In this study, it was studied with data set consisting 52 variables order measure ...

The constitutive androstane receptor (CAR) not only displays a high basal transcriptional activity but also acts as a ligand-dependent transcriptional factor. It is known that CAR exhibits different ligand profiles across species. However, the mechanisms underlying CAR activation by chemicals and the species-specific responses are not fully understood. The objectives of this study are to establ...

We propose Conditional Imputation GAN, an extended missing data imputation method based on Generative Adversarial Networks (GANs). The motivating use case is learning-to-rank, the cornerstone of modern search, recommendation system, and information retrieval applications. Empirical ranking datasets do not always follow standard Gaussian distributions or Missing Completely At Random (MCAR) mecha...