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A Two-Step Feature Selection Method to Predict Cancerlectins by Multiview Features and Synthetic Minority Oversampling Technique
المؤلفون المشاركون
Zhang, Chengjin
Zhang, Lina
Gao, Rui
Yang, Runtao
المصدر
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-02-07
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
الملخص EN
Cancerlectins have an inhibitory effect on the growth of cancer cells and are currently being employed as therapeutic agents.
The accurate identification of the cancerlectins should provide insight into the molecular mechanisms of cancers.
In this study, a new computational method based on the RF (Random Forest) algorithm is proposed for further improving the performance of identifying cancerlectins.
Hybrid feature space before feature selection is developed by combining different individual feature spaces, CTD (Composition, Transition, and Distribution), PseAAC (Pseudo Amino Acid Composition), PSSM (Position-Specific Scoring Matrix), and disorder.
The SMOTE (Synthetic Minority Oversampling Technique) is applied to solve the imbalanced data problem.
To reduce feature redundancy and computation complexity, we propose a two-step feature selection process to select informative features.
A 5-fold cross-validation technique is used for the evaluation of various prediction strategies.
The proposed method achieves a sensitivity of 0.779, a specificity of 0.717, an accuracy of 0.748, and an MCC (Matthew’s Correlation Coefficient) of 0.497.
The prediction results are also compared with other existing methods on the same dataset using 5-fold cross-validation.
The comparison results demonstrate the high effectiveness of our method for predicting cancerlectins.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Yang, Runtao& Zhang, Chengjin& Zhang, Lina& Gao, Rui. 2018. A Two-Step Feature Selection Method to Predict Cancerlectins by Multiview Features and Synthetic Minority Oversampling Technique. BioMed Research International،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1129698
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Yang, Runtao…[et al.]. A Two-Step Feature Selection Method to Predict Cancerlectins by Multiview Features and Synthetic Minority Oversampling Technique. BioMed Research International No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1129698
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Yang, Runtao& Zhang, Chengjin& Zhang, Lina& Gao, Rui. A Two-Step Feature Selection Method to Predict Cancerlectins by Multiview Features and Synthetic Minority Oversampling Technique. BioMed Research International. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1129698
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1129698
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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