| Literature DB >> 19169652 |
Hao Lin1, Hao Wang, Hui Ding, Ying-Li Chen, Qian-Zhong Li.
Abstract
Apoptosis proteins play an essential role in regulating a balance between cell proliferation and death. The successful prediction of subcellular localization of apoptosis proteins directly from primary sequence is much benefited to understand programmed cell death and drug discovery. In this paper, by use of Chou's pseudo amino acid composition (PseAAC), a total of 317 apoptosis proteins are predicted by support vector machine (SVM). The jackknife cross-validation is applied to test predictive capability of proposed method. The predictive results show that overall prediction accuracy is 91.1% which is higher than previous methods. Furthermore, another dataset containing 98 apoptosis proteins is examined by proposed method. The overall predicted successful rate is 92.9%.Entities:
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Year: 2009 PMID: 19169652 DOI: 10.1007/s10441-008-9067-4
Source DB: PubMed Journal: Acta Biotheor ISSN: 0001-5342 Impact factor: 1.774