ArticleBMC bioinformatics2020
Subcellular location prediction of apoptosis proteins using two novel feature extraction methods based on evolutionary information and LDA.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 19 citations in OpenAlex.
- A multi-label cascade flexible neural forest model for predicting the subcellular location of multi-site bacterial proteins.Scientific reports · 2026Article
- Enhancing the Feature Representation of Protein Sequence Descriptors in Protein-Protein Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2025Article
- Protein subcellular localization prediction tools.Computational and structural biotechnology journal · 2024Review
- A Review for Artificial Intelligence Based Protein Subcellular Localization.Biomolecules · 2024Review
- Total glucosides of paeony ameliorates oxidative stress, apoptosis and inflammatory response by regulating the Smad7‑TGF‑β pathway in allergic rhinitis.Molecular medicine reports · 2022Article
- Ensemble of Multiple Classifiers for Multilabel Classification of Plant Protein Subcellular Localization.Life (Basel, Switzerland) · 2021Article
- Predicting Cell Wall Lytic Enzymes Using Combined Features.Frontiers in bioengineering and biotechnology · 2020Article
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Authors and funding
4 authors at 1 institution in 1 country.
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Abstract
backgroundApoptosis, also called programmed cell death, refers to the spontaneous and orderly death of cells controlled by genes in order to maintain a stable internal environment. Identifying the subcellular location of apoptosis proteins is very helpful in understanding the mechanism of apoptosis and designing drugs. Therefore, the subcellular localization of apoptosis proteins has attracted increased attention in computational biology. Effective feature extraction methods play a critical role in predicting the subcellular location of proteins.
resultsIn this paper, we proposed two novel feature extraction methods based on evolutionary information. One of the features obtained the evolutionary information via the transition matrix of the consensus sequence (CTM). And the other utilized the evolutionary information from PSSM based on absolute entropy correlation analysis (AECA-PSSM). After fusing the two kinds of features, linear discriminant analysis (LDA) was used to reduce the dimension of the proposed features. Finally, the support vector machine (SVM) was adopted to predict the protein subcellular locations. The proposed CTM-AECA-PSSM-LDA subcellular location prediction method was evaluated using the CL317 dataset and ZW225 dataset. By jackknife test, the overall accuracy was 99.7% (CL317) and 95.6% (ZW225) respectively.
conclusionsThe experimental results show that the proposed method which is hopefully to be a complementary tool for the existing methods of subcellular localization, can effectively extract more abundant features of protein sequence and is feasible in predicting the subcellular location of apoptosis proteins.
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