Improving quality of protein sequence clustering by noisy relationship detection

• Main Authors: Guan-Hau Chen, 陳冠豪
• Other Authors: Chien-Yu Chen
• Format: Others
• Language: zh-TW
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/23969632550943331924

碩士 === 元智大學 === 生物科技暨生物資訊研究所 === 93 === Protein sequence clustering plays a quite important role in analyzing and predicting the functions and structures of proteins. With employing the transitivity of homology property, the state of the art protein sequence clustering algorithms are able to detect remote homologues, but at the same time turn some multi-domain proteins into noises, degrading the quality of clustering results. Thus, it is believed that detecting multi-domain proteins and blocking their transitivity during clustering will improve overall performance. This thesis studied two previously published methods of detecting multi-domain proteins and tested whether those proteins are really helpful in reducing the noises of clustering. We further proposed a mechanism of detecting noisy relationships based on cluster hierarchies in this thesis. The experimental results show that the information found by our approach is helpful in improving the quality of protein hierarchies. We observed that the proteins identified by the previously published methods present stronger correlation to the multi-domain or multi-functional proteins than the proteins identified by our approach, but it is concluded in this thesis that detecting multi-domain proteins is not apparently helpful in improving the clustering accuracy.