Prediction of Protein-Protein Interaction Strength Using Domain Features with Supervised Regression

المؤلفون المشاركون

Hayashida, Morihiro
Kamada, Mayumi
Sakuma, Yusuke
Akutsu, Tatsuya

المصدر

The Scientific World Journal

العدد

المجلد 2014، العدد 2014 (31 ديسمبر/كانون الأول 2014)، ص ص. 1-7، 7ص.

الناشر

Hindawi Publishing Corporation

تاريخ النشر

2014-06-24

دولة النشر

مصر

عدد الصفحات

7

التخصصات الرئيسية

الطب البشري
تكنولوجيا المعلومات وعلم الحاسوب

الملخص EN

Proteins in living organisms express various important functions by interacting with other proteins and molecules.

Therefore, many efforts have been made to investigate and predict protein-protein interactions (PPIs).

Analysis of strengths of PPIs is also important because such strengths are involved in functionality of proteins.

In this paper, we propose several feature space mappings from protein pairs using protein domain information to predict strengths of PPIs.

Moreover, we perform computational experiments employing two machine learning methods, support vector regression (SVR) and relevance vector machine (RVM), for dataset obtained from biological experiments.

The prediction results showed that both SVR and RVM with our proposed features outperformed the best existing method.

نمط استشهاد جمعية علماء النفس الأمريكية (APA)

Kamada, Mayumi& Sakuma, Yusuke& Hayashida, Morihiro& Akutsu, Tatsuya. 2014. Prediction of Protein-Protein Interaction Strength Using Domain Features with Supervised Regression. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-1048854

نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)

Kamada, Mayumi…[et al.]. Prediction of Protein-Protein Interaction Strength Using Domain Features with Supervised Regression. The Scientific World Journal No. 2014 (2014), pp.1-7.
https://search.emarefa.net/detail/BIM-1048854

نمط استشهاد الجمعية الطبية الأمريكية (AMA)

Kamada, Mayumi& Sakuma, Yusuke& Hayashida, Morihiro& Akutsu, Tatsuya. Prediction of Protein-Protein Interaction Strength Using Domain Features with Supervised Regression. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-1048854

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

Includes bibliographical references

رقم السجل

BIM-1048854