We have applied a little-known data transformation to subsets of the Surveillance, Epidemiology, and End Results (SEER) publically available data of the National Cancer Institute (NCI) to make it suitable input to standard machine learning classifiers. This transformation properly treats the right-censored data in the SEER data and the resulting Random Forest and Multi-Layer Perceptron models p...

In view of the essential role played by dosRS in the survival of Mycobacterium in the infected granuloma cells, dosRS transcriptional regulatory proteins were considered as a validated target for high throughput screening (HTS). However, the cost and time factor involved in screening large compound libraries are an important hurdle in identifying lead compounds. Therefore, the use of computatio...

Multivariate imputation by chained equations (MICE) is commonly used for imputing missing data in epidemiologic research. The "true" imputation model may contain nonlinearities which are not included in default imputation models. Random forest imputation is a machine learning technique which can accommodate nonlinearities and interactions and does not require a particular regression model to be...

BACKGROUND Necrosis of skin flaps is considered as an important complication in reconstructive surgery. We conducted an experimental study to investigate the efficacy of low-molecular weight heparin, clopidogrel and their combination to improve the flap survival. METHODS Forty male, adult Sprague-Dawlay rats were divided randomly into 4 groups. Standard rectangular, distally based dorsal ran...

A model for the classification of 70 HIV-1 protease crystal structure binding pockets to one of its complexed FDA approved protease inhibitors utilizing Random Forest has been developed. 456 chemical descriptors of the binding pocket of each crystal structure have been computed and are used to develop the classification model. Simulations were performed to determine the optimal Random Forest mo...

This study explores a semi-supervised classification approach using random forest as a base classifier to classify the low-back disorders (LBDs) risk associated with the industrial jobs. Semi-supervised classification approach uses unlabeled data together with the small number of labelled data to create a better classifier. The results obtained by the proposed approach are compared with those o...