Abstract：
:Apoptosis proteins are related to many diseases. Obtaining the subcellular localization information of apoptosis proteins is helpful to understand the mechanism of diseases and to develop new drugs. At present, the researchers mainly focus on the primary protein sequences, so there is still room for improvement in the prediction accuracy of the subcellular localization of apoptosis proteins. In this paper, a new method named ERT-ECT-PSSM-IS is proposed to predict apoptosis proteins based on the position-specific scoring matrix (PSSM). First, the local and global features of different directions are extracted by evolutionary row transformation (ERT) and cross-covariance of evolutionary column transformation (ECT) based on PSSM (ERT-ECT-PSSM). Second, an improved isometric mapping algorithm (I-SMA) is used to eliminate redundant features. Finally, we adopt a support vector machine (SVM) to classify our results, and the prediction accuracy is evaluated by jackknife cross-validation tests. The experimental results show that the proposed method not only extracts more abundant feature expression but also has better predictive performance and robustness for the subcellular localization of apoptosis proteins in ZD98, ZW225, and CL317 databases. Graphical abstract Framework of the proposed prediction model.
journal_name
Med Biol Eng Computjournal_title
Medical & biological engineering & computingauthors
Ruan X,Zhou D,Nie R,Hou R,Cao Zdoi
10.1007/s11517-019-02045-3subject
Has Abstractpub_date
2019-12-01 00:00:00pages
2553-2565issue
12eissn
0140-0118issn
1741-0444pii
10.1007/s11517-019-02045-3journal_volume
57pub_type
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