An Overview of Data Mining Algorithms in Drug Induced Toxicity Prediction

Title:An Overview of Data Mining Algorithms in Drug Induced Toxicity Prediction

Volume: 14 Issue: 4

Author(s): Ankur Omer, Poonam Singh, N.K. Yadav and R.K. Singh

Affiliation:

Keywords: Bioinformatics, computational prediction, data mining, in silico, machine learning, toxicity prediction.

Abstract: The growth in chemical diversity has increased the need to adjudicate the toxicity of different chemical compounds raising the burden on the demand of animal testing. The toxicity evaluation requires time consuming and expensive undertaking, leading to the deprivation of the methods employed for screening chemicals pointing towards the need to develop more efficient toxicity assessment systems. Computational approaches have reduced the time as well as the cost for evaluating the toxicity and kinetic behavior of any chemical. The accessibility of a large amount of data and the intense need of turning this data into useful information have attracted the attention towards data mining. Machine Learning, one of the powerful data mining techniques has evolved as the most effective and potent tool for exploring new insights on combinatorial relationships among various experimental data generated. The article accounts on some sophisticated machine learning algorithms like Artificial Neural Networks (ANN), Support Vector Machine (SVM), k-mean clustering and Self Organizing Maps (SOM) with some of the available tools used for classification, sorting and toxicological evaluation of data, clarifying, how data mining and machine learning interact cooperatively to facilitate knowledge discovery. Addressing the association of some commonly used expert systems, we briefly outline some real world applications to consider the crucial role of data set partitioning.

Cite this article as:

Omer Ankur, Singh Poonam, Yadav N.K. and Singh R.K., An Overview of Data Mining Algorithms in Drug Induced Toxicity Prediction, Mini-Reviews in Medicinal Chemistry 2014; 14 (4) . https://dx.doi.org/10.2174/1389557514666140219110244