Title :
Subcellular localization prediction of apoptosis proteins based on the data mining for amino acid index database
Author :
Zhuo-xing Shi ; Qi Dai ; Ping-an He ; Yu-hua Yao ; Bo Liao
Author_Institution :
Coll. of Life Sci., Zhejiang Sci-Tech Univ., Hangzhou, China
Abstract :
In this work, based on the ACF model and the SVM classifier, succeeded on trials mining information that it´s more effective to analyze the subcellular localization prediction of apoptosis proteins when adopting hydrophobicity property. This information is obtained in three benchmark datasets by using the ACF model and SVM to scan the AAindex database, which contains 544 kinds of amino acids. The contribution of this work is that it first did a comprehensive research on the effectiveness of the amino acid index for the subcellular localization of apoptosis proteins.
Keywords :
bioinformatics; cellular biophysics; data mining; hydrophobicity; molecular biophysics; proteins; support vector machines; ACF model; SVM classifier; amino acid index database; data mining; hydrophobicity property; protein apoptosis; subcellular localization prediction; support vector machine; Accuracy; Amino acids; Indexes; Principal component analysis; Proteins; Support vector machines; Amino acid index; Apoptosis protein; Database mining; Subcellular localization prediction;
Conference_Titel :
Systems Biology (ISB), 2013 7th International Conference on
Conference_Location :
Huangshan
DOI :
10.1109/ISB.2013.6623792