paper 9, Boosting the Performance of Pseudo Amino Acid Composition
Table of Contents
- Session 1 of Future Computer and Communication 2011
- 1. Integrating Entanglement Swapping into Secure Socket Layer — Handshake Protocol
- Session 2 of Future Computer and Communication 2011
- 11. Scene Change Detection with Temporally Constrained Clustering
- Session 3 of Future Computer and Communication 2011
- 21. Walking Compensation Treadmill Based System: Device, Environment and Testing Method
- Session 4 of Future Computer and Communication 2011
- 31. Hidden Coupling and Its Impact on Software Reliability
- Session 1 of Mathematics in Business and Economics 2011
- 40. Modeling Operational Risk in Financial Institutions: Application and Improvement on EVT
- 44. The Effects of Split Share Structure on Accounting Conservatism—Evidence from Chinese Listed Firms
- Session 1 of Mathematics and Geosciences 2011
- 46. Application of Three-Dimensional Terrain Modeling Technology of Gan Jiang Yuan
- Session 1 of Mathematics and Arts 2011
- 51. Automatic Masked Morphing for 3D Facial Animations
- Session 2 of Mathematics and Arts 2011
- 61. Structural Instability for Solo Piano (2007) by Greek Composer Fani Kosona
- Session 3 of Mathematics and Arts 2011
- 71. The Creation of the Modal through Symmetry in Liviu Glodeanu's Musical Composition
Chapter Contents
- Abstract
- Key Words
- 1 Introduction
- 2. Proposed System
- 3. Experimental Results
- 4. Conclusion
- References
Excerpt
Protein-protein interactions are critical in coordinating various cellular processes. They help understanding protein function and drug design. Extracting protein features from amino acid sequences is important in order to study protein-protein interactions. Various feature extraction approaches for proteins have been introduced up to the present. PseAAC is one of the most used protein feature extractor. In this work we purpose a new approach to calculate amino acid composition values. The purpose of our method is to adjust the weights of the composition values during feature extraction process. It means that bigger composition values will contribute more to prediction function than smaller ones. Our experimental results showed that our method outperformed PseAAC.
©2011 ASME


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