A Classification Scheme for Predicting the Subcellular Localization of the Apoptosis Proteins Using Composition Features and Multiscale Entropy

Apoptosis proteins carry out the mechanism of regulated cell destruction. To understand the process of apoptosis, it is important to classify the apoptosis proteins based on their subcellular location. Computer-assisted automatic detection of the subcellular location of the apoptosis proteins from their amino acid sequences can replace laboratory experiment which is both expensive and time-consuming. In this paper, the classification of apoptosis proteins based on their subcellular localization is presented. Various composition based features and multiscale entropy-based features are extracted. Neighborhood Component Analysis (NCA) is employed to select the features with high significance. Two well-known classifiers, the Support Vector Machine (SVM) and the K-Nearest Neighbor (KNN) are employed to classify the proteins from the reduced feature set. The proposed method is validated using a widely used public database for apoptosis proteins. Well-known figures of merit such as accuracy, sensitivity, specificity and area under the curve (AUC) are analyzed and compared with state-of-the-art methods in the literature. It is seen that the proposed method shows promising results in terms of accuracy as well as sensitivity for each class and can be used as an automatic support system for researchers in identifying the subcellular location of apoptosis proteins.

• Publication year

  2018

• Venue

  International Conference on Electrical and Control Engineering

• Publication date

  2018-12-01

• Fields of study

  Biology, Computer Science

• Identifiers
  DOI 10.1109/ICECE.2018.8636708
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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