Subcellular location prediction of apoptosis proteins using two novel feature extraction methods based on evolutionary information and LDA

Du, Lei; Meng, Qingchun; Chen, Yuehui; Wu, Peng

doi:10.1186/s12859-020-3539-1

Cited by 14 publications

(9 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…High-dimensional features that include much redundant information might harm and negatively influence the performance of the classifier. Dimensionality reduction algorithms can help to eliminate redundant data from the original feature space and are widely used in machine learning [ 32 ]. Therefore, a feature-selection step is needed to identify discriminative and nonredundant feature subsets that can discriminate all locations.…”

Section: Resultsmentioning

confidence: 99%

“…Diverse subcellular localization computational prediction tools were proposed using different training data procedures, data features, and machine learning algorithms [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ]. Some tools used the support vector machine (SVM) algorithm such as MultiLoc2 [ 10 ], Plant-mSubP [ 11 ], mGOASVM [ 12 ], WegoLoc [ 13 ], and LocTree [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

“…There is evidence indicating that proteins with multiple locations might present specific biological functions that are intriguing to investigators [ 31 ]. Therefore, it is necessary to explore features and appropriate classification methods for improving prediction performance [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Ensemble of Multiple Classifiers for Multilabel Classification of Plant Protein Subcellular Localization

Wattanapornprom

Thammarongtham

Hongsthong

et al. 2021

Life

View full text Add to dashboard Cite

The accurate prediction of protein localization is a critical step in any functional genome annotation process. This paper proposes an improved strategy for protein subcellular localization prediction in plants based on multiple classifiers, to improve prediction results in terms of both accuracy and reliability. The prediction of plant protein subcellular localization is challenging because the underlying problem is not only a multiclass, but also a multilabel problem. Generally, plant proteins can be found in 10–14 locations/compartments. The number of proteins in some compartments (nucleus, cytoplasm, and mitochondria) is generally much greater than that in other compartments (vacuole, peroxisome, Golgi, and cell wall). Therefore, the problem of imbalanced data usually arises. Therefore, we propose an ensemble machine learning method based on average voting among heterogeneous classifiers. We first extracted various types of features suitable for each type of protein localization to form a total of 479 feature spaces. Then, feature selection methods were used to reduce the dimensions of the features into smaller informative feature subsets. This reduced feature subset was then used to train/build three different individual models. In the process of combining the three distinct classifier models, we used an average voting approach to combine the results of these three different classifiers that we constructed to return the final probability prediction. The method could predict subcellular localizations in both single- and multilabel locations, based on the voting probability. Experimental results indicated that the proposed ensemble method could achieve correct classification with an overall accuracy of 84.58% for 11 compartments, on the basis of the testing dataset.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Ensemble of Multiple Classifiers for Multilabel Classification of Plant Protein Subcellular Localization

Wattanapornprom

Thammarongtham

Hongsthong

et al. 2021

Life

View full text Add to dashboard Cite

show abstract

“…Cell apoptosis refers to spontaneous and orderly death of cells to maintain homeostasis, involving the activation, expression and regulation of a series of genes including Bax and Bcl-2 ( 39 , 40 ). Cell apoptosis is a key process in AR that is regulated by numerous factors.…”

Section: Discussionmentioning

confidence: 99%

Total glucosides of paeony ameliorates oxidative stress, apoptosis and inflammatory response by regulating the Smad7‑TGF‑β pathway in allergic rhinitis

Jin

Zhang

2022

Mol Med Rep

View full text Add to dashboard Cite

“…The support vector machine was proposed by Vapnik; the basic idea of SVM is to transform the input data into a high-dimensional Hilbert space and then determine the optional separating hyperplane. SVM has been successfully applied in the field of computational biology and bioinformatics ( Fan et al, 2013 ; Li and Wang, 2016 ; Arif et al, 2018 ; Chen et al, 2019 ; Tian et al, 2019 ; Wang et al, 2019a ; Du et al, 2020 ; Jing and Li, 2020 ; Yang et al, 2020 ). Therefore, we used this classifier to build our model.…”

Section: Methodsmentioning

confidence: 99%

Predicting Cell Wall Lytic Enzymes Using Combined Features

Jing

2021

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

Due to the overuse of antibiotics, people are worried that existing antibiotics will become ineffective against pathogens with the rapid rise of antibiotic-resistant strains. The use of cell wall lytic enzymes to destroy bacteria has become a viable alternative to avoid the crisis of antimicrobial resistance. In this paper, an improved method for cell wall lytic enzymes prediction was proposed and the amino acid composition (AAC), the dipeptide composition (DC), the position-specific score matrix auto-covariance (PSSM-AC), and the auto-covariance average chemical shift (acACS) were selected to predict the cell wall lytic enzymes with support vector machine (SVM). In order to overcome the imbalanced data classification problems and remove redundant or irrelevant features, the synthetic minority over-sampling technique (SMOTE) was used to balance the dataset. The F-score was used to select features. The Sn, Sp, MCC, and Acc were 99.35%, 99.02%, 0.98, and 99.19% with jackknife test using the optimized combination feature AAC+DC+acACS+PSSM-AC. The Sn, Sp, MCC, and Acc of cell wall lytic enzymes in our predictive model were higher than those in existing methods. This improved method may be helpful for protein function prediction.

show abstract

Subcellular location prediction of apoptosis proteins using two novel feature extraction methods based on evolutionary information and LDA

Cited by 14 publications

References 57 publications

Ensemble of Multiple Classifiers for Multilabel Classification of Plant Protein Subcellular Localization

Ensemble of Multiple Classifiers for Multilabel Classification of Plant Protein Subcellular Localization

Total glucosides of paeony ameliorates oxidative stress, apoptosis and inflammatory response by regulating the Smad7‑TGF‑β pathway in allergic rhinitis

Predicting Cell Wall Lytic Enzymes Using Combined Features

Contact Info

Product

Resources

About