Type of Document Dissertation Author Zhong, Wei Author's Email Address jetzhong@yahoo.com URN etd-07242006-200443 Title Clustering System and Clustering Support Vector Machine for Local Protein Structure Prediction Degree Ph.D. Department Computer Science Advisory Committee
Advisor Name Title Yi Pan Committee Chair Martin Fraser Committee Member Phang C. Tai Committee Member Robert Harrison Committee Member Keywords
- granular computing
- SVM (Support Vector Machine)
- K-means clustering algorithm
- sequence motif
- protein structure prediction
Date of Defense 2006-05-23 Availability unrestricted Abstract Protein tertiary structure plays a very important role in determining its possible functional sites and chemical interactions with other relatedproteins. Experimental methods to determine protein structure are time consuming and expensive. As a result, the gap between protein sequence and its structure has widened substantially due to the high throughput sequencing techniques. Problems of experimental methods motivate us to develop the computational algorithms for protein structure prediction.
In this work, the clustering system is used to predict local protein structure. At first, recurring sequence clusters are explored with an improved K-means clustering algorithm. Carefully constructed sequence clusters are used to predict local protein structure. After obtaining the sequence clusters and motifs, we study how sequence variation for sequence clusters may influence its structural similarity.
Analysis of the relationship between sequence variation and structural similarity for sequence clusters shows that sequence clusters with tight sequence variation have high structural similarity and sequence clusters with wide sequence variation have poor structural similarity. Based on above knowledge, the established clustering system is used to predict the tertiary structure for local sequence segments. Test results indicate that highest quality clusters can give highly reliable prediction results and high quality clusters can give reliable prediction results.
In order to improve the performance of the clustering system for local protein structure prediction, a novel computational model called Clustering Support Vector Machines (CSVMs) is proposed. In our previous work, the sequence-to-structure relationship with the K-means algorithm has been explored by the conventional K-means algorithm. The K-means clustering algorithm may not capture nonlinear sequence-to-structure relationship effectively. As a result, we consider using Support Vector Machine (SVM) to capture the nonlinear sequence-to-structure relationship. However, SVM is not favorable for huge datasets including millions of samples. Therefore, we propose a novel computational model called CSVMs. Taking advantage of both the theory of granular computing and advanced statistical learning methodology, CSVMs are built specifically for each information granule partitioned intelligently by the clustering algorithm. Compared with the clustering system introduced previously, our experimental results show that accuracy for local structure prediction has been improved noticeably when CSVMs are applied.
Files
Filename Size Approximate Download Time (Hours:Minutes:Seconds)
28.8 Modem 56K Modem ISDN (64 Kb) ISDN (128 Kb) Higher-speed Access zhong_wei_200608_phd.pdf 668.33 Kb 00:03:05 00:01:35 00:01:23 00:00:41 00:00:03