Determining Cutoff Point of Ensemble Trees Based on Sample Size in Predicting Clinical Dose with DNA Microarray Data

Background/Aim. Evaluating the success of dose prediction based on genetic or clinical data has substantially advanced recently. The aim of this study is to predict various clinical dose values from DNA gene expression datasets using data mining techniques. Materials and Methods. Eleven real gene expression datasets containing dose values were included. First, important genes for dose prediction were selected using iterative sure independence screening. Then, the performances of regression trees (RTs), support vector regression (SVR), RT bagging, SVR bagging, and RT boosting were examined. Results. The results demonstrated that a regression-based feature selection method substantially reduced the number of irrelevant genes from raw datasets. Overall, the best prediction performance in nine of 11 datasets was achieved using SVR; the second most accurate performance was provided using a gradient-boosting machine (GBM). Conclusion. Analysis of various dose values based on microarray gene expression data identified common genes found in our study and the referenced studies. According to our findings, SVR and GBM can be good predictors of dose-gene datasets. Another result of the study was to identify the sample size of n=25 as a cutoff point for RT bagging to outperform a single RT

Gmdh2: Binary Classification Via Gmdh-Type Neural Network Algorithms-R Package And Web-Based Tool

Group method of data handling (GMDH)-type neural network algorithms are the self-organizing algorithms for modeling complex systems. GMDH algorithms are used for different objectives; examples include regression, classification, clustering, forecasting, and so on. In this paper, we present GMDH2 package to perform binary classification via GMDH-type neural network algorithms. The package offers two main algorithms: GMDH algorithm and diverse classifiers ensemble based on GMDH (dce-GMDH) algorithm. GMDH algorithm performs binary classification and returns important variables. dce-GMDH algorithm performs binary classification by assembling classifiers based on GMDH algorithm. The package also provides a well-formatted table of descriptives in different format (R, LaTeX, HTML). Moreover, it produces confusion matrix and related statistics, and scatter plot (2D and 3D) with classification labels of binary classes to assess the prediction performance. Moreover, a user-friendly web-interface of the package is provided especially for non-R users. (c) 2019 The Authors. Published by Atlantis Press SARL.WoSScopu

Diverse classifiers ensemble based on GMDH-type neural network algorithm for binary classification

Group Method of Data Handling (GMDH) - type neural network algorithm is the heuristic self-organizing algorithm to model the sophisticated systems. In this study, we propose a new algorithm assembling different classifiers based on GMDH algorithm for binary classification. A Monte Carlo simulation study is conducted to compare diverse classifier ensemble based on GMDH (dce-GMDH) algorithm to the other well-known classifiers and to give recommendations for applied researchers on the selection of appropriate classifier under the different conditions. The simulation study illustrates the proposed approach is more successful than the other classifiers in classification in most scenarios generated under the different conditions. Our proposed method is compared to the other classifiers on Cleveland heart disease data. An implementation of the proposed approach is demonstrated on urine data. Moreover, the proposed algorithm is released under R package GMDH2 under the name of "dceGMDH" for implementation

GINGIVAL WETNESS IN PATIENTS WITH DRY MOUTH

Introduction: Gingival wetness can be diminished in the presence of dry mouth. Our objective was to comparatively assess oral mucosal/gingival wetness in conjunction with other saliva-related measures, in patients with dry mouth