Earticle

초록

영어

Protein includes many substances, such as enzymes, hormones and antibodies that are necessary for the organisms. These proteins have different shapes and structures which distinct them from each other. By having unique structures, only proteins able to carried out their function efficiently. The importance of understanding protein structure has fueled the development of protein structure databases and prediction tools. The main objective of this research is to optimize local protein structure with Support Vector Machine (SVM) to predict protein secondary structure. Most of the related study used fixed segment length for secondary structure prediction and this might produce inaccurate results. In this research, dataset is segmented into different segment length of local protein structure. An optimal length of local protein structure is determined and the evaluation is carried out by comparing with the existing methods and initial prediction using native structure. Higher accuracy and true positive rate, low false positive rate are obtained which prove the effectiveness of this prediction method. A statistical method, t-test, is applied to validate the results of the prediction.

목차

Abstract
 1. Introduction
 2. Materials and Methods
  2.1. Dataset
  2.2. Dihedral Angle (DA)
  2.3. DSSP
  2.4. Methods Using Support Vector Machine
  2.5. Performance Measurement
 3. Results and Discussions
 4. Conclusion
 References

키워드

저자

• Chin Yin Fai [ Artificial Intelligence and Bioinformatics Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia ]
• Rohayanti Hassan [ Artificial Intelligence and Bioinformatics Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia ]
• Mohd Saberi Mohamad [ Artificial Intelligence and Bioinformatics Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Bio-Science and Bio-Technology

• 간기

  격월간

• pISSN

  2233-7849

• 수록기간

  2009~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605