Abstract:
:Mitochondria are essential cell organelles of eukaryotes. Hence, it is vitally important to develop an automated and reliable method for timely identification of novel mitochondrial proteins. In this study, mitochondrial proteins were encoded by dipeptide composition technology; then, the genetic algorithm-partial least square (GA-PLS) method was used to evaluate the dipeptide composition elements which are more important in recognizing mitochondrial proteins; further, these selected dipeptide composition elements were applied to support vector machine (SVM)-based classifiers to predict the mitochondrial proteins. All the models were trained and validated by the jackknife cross-validation test. The prediction accuracy is 85%, suggesting that it performs reasonably well in predicting the mitochondrial proteins. Our results strongly imply that not all the dipeptide compositions are informative and indispensable for predicting proteins. The source code of MATLAB and the dataset are available on request under liml@scu.edu.cn.
journal_name
Amino Acidsjournal_title
Amino acidsauthors
Tan F,Feng X,Fang Z,Li M,Guo Y,Jiang Ldoi
10.1007/s00726-006-0465-0subject
Has Abstractpub_date
2007-11-01 00:00:00pages
669-75issue
4eissn
0939-4451issn
1438-2199journal_volume
33pub_type
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