![](/images/graphics-bg.png)
Identifying Heat Shock Protein Families from Imbalanced Data by Using Combined Features
المؤلفون المشاركون
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-09-23
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
Heat shock proteins (HSPs) are ubiquitous in living organisms.
HSPs are an essential component for cell growth and survival; the main function of HSPs is controlling the folding and unfolding process of proteins.
According to molecular function and mass, HSPs are categorized into six different families: HSP20 (small HSPS), HSP40 (J-proteins), HSP60, HSP70, HSP90, and HSP100.
In this paper, improved methods for HSP prediction are proposed—the split amino acid composition (SAAC), the dipeptide composition (DC), the conjoint triad feature (CTF), and the pseudoaverage chemical shift (PseACS) were selected to predict the HSPs with a support vector machine (SVM).
In order to overcome the imbalance data classification problems, the syntactic minority oversampling technique (SMOTE) was used to balance the dataset.
The overall accuracy was 99.72% with a balanced dataset in the jackknife test by using the optimized combination feature SAAC+DC+CTF+PseACS, which was 4.81% higher than the imbalanced dataset with the same combination feature.
The Sn, Sp, Acc, and MCC of HSP families in our predictive model were higher than those in existing methods.
This improved method may be helpful for protein function prediction.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Jing, Xiao-Yang& Li, Feng-Min. 2020. Identifying Heat Shock Protein Families from Imbalanced Data by Using Combined Features. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1139648
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Jing, Xiao-Yang& Li, Feng-Min. Identifying Heat Shock Protein Families from Imbalanced Data by Using Combined Features. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1139648
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Jing, Xiao-Yang& Li, Feng-Min. Identifying Heat Shock Protein Families from Imbalanced Data by Using Combined Features. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1139648
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1139648
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
![](/images/ebook-kashef.png)
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
![](/images/kashef-image.png)