mGOF-loc: A novel ensemble learning method for human protein subcellular localization prediction

Wei, Leyi; Liao, Minghong; Gao, Xing; Wang, Jingjing; Lin, Wenming

doi:10.1016/j.neucom.2015.09.137

Cited by 29 publications

(18 citation statements)

References 81 publications

(61 reference statements)

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“…Protein subcellular location research is a hot issue in cell biology and bioinformatics. Therefore, new excellent approaches based on machine learning have merged, such as Hum‐mPLoc 2.0, mGOF‐Loc, IMMMLGP, and Xiaotong Guo's approach . According to Table , the first two methods are based on a single‐label source, whereas the last two methods are based on a multi‐label source.…”

Section: Resultsmentioning

confidence: 99%

HPSLPred: An Ensemble Multi‐Label Classifier for Human Protein Subcellular Location Prediction with Imbalanced Source

2017

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Protein subcellular localization prediction is an important and challenging problem. The traditional biology experiments are expensive and time-consuming, so more and more research interests tend to a series of machine learning approaches for predicting protein subcellular location. There are two main difficult problems among the existing state-of-the-art methods. First, most of the existing techniques are designed to deal with the multi-class but not the multi-label classification, which ignores the connection between the multiple labels. In reality, multiple location proteins implicate that there are vital and unique biological significances worthy of special focus, which cannot be ignored. Second, the techniques for handling imbalanced data in multi-label classification problem is significant but less. For solving the two issues, we have developed an ensemble multi-label classifier called HPSLPred which can be applied for the multi-label classification with imbalanced protein source. For the conveniences of users, a user-friendly webserver for HPSLPred was established at http://server.malab.cn/HPSLPred.

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Section: Resultsmentioning

confidence: 99%

HPSLPred: An Ensemble Multi‐Label Classifier for Human Protein Subcellular Location Prediction with Imbalanced Source

2017

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“…In order to prove the performance of our method, we compared with the latest protein subcellular localization web servers, including IMMMLGP 28 , Hum-mPLoc 2.0 40 , mGOF-Loc 52 . The first one is a multi-label classifier, while the other two can only predict as single class.…”

Section: Resultsmentioning

confidence: 99%

Human Protein Subcellular Localization with Integrated Source and Multi-label Ensemble Classifier

Guo

Liu

et al. 2016

Sci Rep

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Predicting protein subcellular location is necessary for understanding cell function. Several machine learning methods have been developed for computational prediction of primary protein sequences because wet experiments are costly and time consuming. However, two problems still exist in state-of-the-art methods. First, several proteins appear in different subcellular structures simultaneously, whereas current methods only predict one protein sequence in one subcellular structure. Second, most software tools are trained with obsolete data and the latest new databases are missed. We proposed a novel multi-label classification algorithm to solve the first problem and integrated several latest databases to improve prediction performance. Experiments proved the effectiveness of the proposed method. The present study would facilitate research on cellular proteomics.

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“…e location of proteins in organisms is closely related to their function and disease can occur following deviations in protein location [1]. e prediction of apoptosis protein localization began in 2003; this is a significant part of proteomics and one of the hotspots of bioinformatics [2].…”

Section: Introductionmentioning

confidence: 99%

Predictions of Apoptosis Proteins by Integrating Different Features Based on Improving Pseudo-Position-Specific Scoring Matrix

Ruan

Zhou

Nie

et al. 2020

BioMed Research International

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Apoptosis proteins are strongly related to many diseases and play an indispensable role in maintaining the dynamic balance between cell death and division in vivo. Obtaining localization information on apoptosis proteins is necessary in understanding their function. To date, few researchers have focused on the problem of apoptosis data imbalance before classification, while this data imbalance is prone to misclassification. Therefore, in this work, we introduce a method to resolve this problem and to enhance prediction accuracy. Firstly, the features of the protein sequence are captured by combining Improving Pseudo-Position-Specific Scoring Matrix (IM-Psepssm) with the Bidirectional Correlation Coefficient (Bid-CC) algorithm from position-specific scoring matrix. Secondly, different features of fusion and resampling strategies are used to reduce the impact of imbalance on apoptosis protein datasets. Finally, the eigenvector adopts the Support Vector Machine (SVM) to the training classification model, and the prediction accuracy is evaluated by jackknife cross-validation tests. The experimental results indicate that, under the same feature vector, adopting resampling methods remarkably boosts many significant indicators in the unsampling method for predicting the localization of apoptosis proteins in the ZD98, ZW225, and CL317 databases. Additionally, we also present new user-friendly local software for readers to apply; the codes and software can be freely accessed at https://github.com/ruanxiaoli/Im-Psepssm.

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mGOF-loc: A novel ensemble learning method for human protein subcellular localization prediction

Cited by 29 publications

References 81 publications

HPSLPred: An Ensemble Multi‐Label Classifier for Human Protein Subcellular Location Prediction with Imbalanced Source

HPSLPred: An Ensemble Multi‐Label Classifier for Human Protein Subcellular Location Prediction with Imbalanced Source

Human Protein Subcellular Localization with Integrated Source and Multi-label Ensemble Classifier

Predictions of Apoptosis Proteins by Integrating Different Features Based on Improving Pseudo-Position-Specific Scoring Matrix

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