ArticleBioMed research international2016
Predicting Subcellular Localization of Apoptosis Proteins Combining GO Features of Homologous Proteins and Distance Weighted KNN Classifier.
Article in BioMed research international, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A hybrid cost-sensitive ensemble for heart disease prediction.BMC medical informatics and decision making · 2021Article
- The SoNAP gene from sugarcane (Saccharum officinarum) encodes a senescence-associated NAC transcription factor involved in response to osmotic and salt stress.Journal of plant research · 2020Article
- Subcellular location prediction of apoptosis proteins using two novel feature extraction methods based on evolutionary information and LDA.BMC bioinformatics · 2020Article
- Prediction of Apoptosis Protein Subcellular Localization with Multilayer Sparse Coding and Oversampling Approach.BioMed research international · 2019Article
- The effect of three novel feature extraction methods on the prediction of the subcellular localization of multi-site virus proteins.Bioengineered · 2018Article
- MultiP-Apo: A Multilabel Predictor for Identifying Subcellular Locations of Apoptosis Proteins.Computational intelligence and neuroscience · 2017Article
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Abstract
Apoptosis proteins play a key role in maintaining the stability of organism; the functions of apoptosis proteins are related to their subcellular locations which are used to understand the mechanism of programmed cell death. In this paper, we utilize GO annotation information of apoptosis proteins and their homologous proteins retrieved from GOA database to formulate feature vectors and then combine the distance weighted KNN classification algorithm with them to solve the data imbalance problem existing in CL317 data set to predict subcellular locations of apoptosis proteins. It is found that the number of homologous proteins can affect the overall prediction accuracy. Under the optimal number of homologous proteins, the overall prediction accuracy of our method on CL317 data set reaches 96.8% by Jackknife test. Compared with other existing methods, it shows that our proposed method is very effective and better than others for predicting subcellular localization of apoptosis proteins.
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