Feature Selection in Quantitative Structure-Activity Relationship (QSAR) Study: Combining Firefly and Neural Network methods

سال انتشار: 1396
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 541

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شناسه ملی سند علمی:

IBIS07_182

تاریخ نمایه سازی: 29 فروردین 1397

چکیده مقاله:

Over the past decade, the use of feature selection methods in bioinformatics has become a major need for model building. This is due to the high dimension of data in various bioinformatics sciences, such as Quantitative Structure-Activity Relationship, which leads to the use of machine learning techniques such as feature selection. Therefore, the use of feature selection methods is inevitable. In this paper, we use a combination method to select important descriptors in a QSAR dataset in drug design. Optimization algorithms and evolutionary random search for feature selection are new and effective methods used to find global optima of problems. The randomness feature of these algorithms prevents clogging in local optima. In this paper, we propose a method for selecting the features with combination of a firefly and a neural network. We use a QSAR data set with 55 instance and 221 attributes to estimate the IC50. Results of experiment obtained in each run, proved that our method selects about 50 percent of the features that give the best result. The proposed method has been able to achieve a good regression with a significant reduction in features. The results show the acceptable accuracy of the proposed method in identifying important descriptors in QSAR

نویسندگان

Masoud Arabfard

University of Tehran, Kish International Campus, Iran

Mazaher Maghsoudloo

University of Tehran, Kish International Campus, Iran

Kaveh kavousi

Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran

Sajjad Gharaghani

Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran