Prediction of genotoxicity of chemical compounds by statistical learning methods.
نویسندگان
چکیده
Various toxicological profiles, such as genotoxic potential, need to be studied in drug discovery processes and submitted to the drug regulatory authorities for drug safety evaluation. As part of the effort for developing low cost and efficient adverse drug reaction testing tools, several statistical learning methods have been used for developing genotoxicity prediction systems with an accuracy of up to 73.8% for genotoxic (GT+) and 92.8% for nongenotoxic (GT-) agents. These systems have been developed and tested by using less than 400 known GT+ and GT- agents, which is significantly less in number and diversity than the 860 GT+ and GT- agents known at present. There is a need to examine if a similar level of accuracy can be achieved for the more diverse set of molecules and to evaluate other statistical learning methods not yet applied to genotoxicity prediction. This work is intended for testing several statistical learning methods by using 860 GT+ and GT- agents, which include support vector machines (SVM), probabilistic neural network (PNN), k-nearest neighbor (k-NN), and C4.5 decision tree (DT). A feature selection method, recursive feature elimination, is used for selecting molecular descriptors relevant to genotoxicity study. The overall accuracies of SVM, k-NN, and PNN are comparable to and those of DT lower than the results from earlier studies, with SVM giving the highest accuracies of 77.8% for GT+ and 92.7% for GT- agents. Our study suggests that statistical learning methods, particularly SVM, k-NN, and PNN, are useful for facilitating the prediction of genotoxic potential of a diverse set of molecules.
منابع مشابه
Thermal conductivity of Water-based nanofluids: Prediction and comparison of models using machine learning
Statistical methods, and especially machine learning, have been increasingly used in nanofluid modeling. This paper presents some of the interesting and applicable methods for thermal conductivity prediction and compares them with each other according to results and errors that are defined. The thermal conductivity of nanofluids increases with the volume fraction and temperature. Machine learni...
متن کاملThermal conductivity of Water-based nanofluids: Prediction and comparison of models using machine learning
Statistical methods, and especially machine learning, have been increasingly used in nanofluid modeling. This paper presents some of the interesting and applicable methods for thermal conductivity prediction and compares them with each other according to results and errors that are defined. The thermal conductivity of nanofluids increases with the volume fraction and temperature. Machine learni...
متن کاملNonresponse prediction in an establishment survey using combination of statistical learning methods
Nonrespose is a source of error in the survey results and national statistical organizations are always looking for ways to control and reduce it. Predicting nonrespons sampling units in the survey before conducting the survey is one of the solutions that can help a lot in reducing and treating the survey nonresponse. Recent advances in technology and the facilitation of complex calculations...
متن کاملPrediction of melting points of a diverse chemical set using fuzzy regression tree
The classification and regression trees (CART) possess the advantage of being able to handlelarge data sets and yield readily interpretable models. In spite to these advantages, they are alsorecognized as highly unstable classifiers with respect to minor perturbations in the training data.In the other words methods present high variance. Fuzzy logic brings in an improvement in theseaspects due ...
متن کاملModeling of Chloride Ion Separation by Nanofiltration Using Machine Learning Techniques
In this work, several machine learning techniques are presented for nanofiltration modeling. According to the results, specific errors are defined. The rejection due to Nanofiltration increases with pressure but decreases with increasing the concentration of chloride ion. Methods of machine learning represent the rejection of nanofiltration as a function of concentration, pH, pressure and also ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Chemical research in toxicology
دوره 18 6 شماره
صفحات -
تاریخ انتشار 2005