Subcellular localization prediction of apoptosis proteins based on evolutionary information and support vector machine

Subcellular localization prediction of apoptosis proteins based on evolutionary information and support vector machine

Highlights • The novel evolutionary-conservative information is introduced to represent protein sequences. • Position-specific scoring matrix is divided into several blocks based on the proportion of golden section. • Our method provides the state-of-the-art performance for predicting subcellular lo...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Artificial intelligence in medicine 2017-05, Vol.78, p.41-46
Hauptverfasser: Xiang, Qilin, Liao, Bo, Li, Xianhong, Xu, Huimin, Chen, Jing, Shi, Zhuoxing, Dai, Qi, Yao, Yuhua
Format: Artikel
Sprache:eng
Schlagworte:
Amino Acid Sequence
Apoptosis
Apoptosis protein
Apoptosis Regulatory Proteins
Computational Biology
Databases, Protein
Evolution, Molecular
Golden section
Internal Medicine
Other
Position-specific scoring matrix
Proteins
Support Vector Machine
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Highlights • The novel evolutionary-conservative information is introduced to represent protein sequences. • Position-specific scoring matrix is divided into several blocks based on the proportion of golden section. • Our method provides the state-of-the-art performance for predicting subcellular location of apoptosis proteins.
ISSN:0933-3657
1873-2860
DOI:10.1016/j.artmed.2017.05.007