MFSC: Multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou's PseAAC components

Jamal, Ahmad; Hayat, Maqsood

doi:10.1016/j.jtbi.2018.12.017

Cited by 48 publications

(28 citation statements)

References 107 publications

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“…In 2017, Ahmad et al [4] have employed Bigram PSSM, split PseAAC, and DPC features in conjunction with SMOTE oversampling and Fisher's feature selection that achieved 94.9% accuracy through jackknife and 10fold cross-validation testing. In continuation of their previous work, Ahmad and Hayat [10] proposed to use DPC with gap 3, SAAC, and PSSM based features in conjunction with SMOTE oversampling technique. Following this, Majority-Voting based Feature Selection (MVFS) is applied that selects high ranked features from the integrated space of selected features with 11 different feature selection techniques.…”

Section: Introductionmentioning

confidence: 91%

See 1 more Smart Citation

Discrimination of Golgi Proteins Through Efficient Exploitation of Hybrid Feature Spaces Coupled With SMOTE and Ensemble of Support Vector Machine

et al. 2020

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Section: Introductionmentioning

confidence: 91%

“…show the balance of classifier over the majority as well as minority classes. G-mean can be calculated using (10).…”

Section: G-meanmentioning

confidence: 99%

Discrimination of Golgi Proteins Through Efficient Exploitation of Hybrid Feature Spaces Coupled With SMOTE and Ensemble of Support Vector Machine

et al. 2020

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“…To avoid completely losing the sequence-pattern information for proteins, the pseudo amino acid composition (PseAAC) [26,33] was proposed. The PseAAC has been widely used in the areas of bioinformatics [34][35][36][37][38][39][40][41][42][43][44]. As PseAAC has been widely and increasingly used, four powerful open-access software, called 'PseAAC' [45], 'PseAAC-Builder' [46], 'Propy' [47], and 'PseAAC-General' [48], were established to generate pseudo amino acid composition features.…”

Section: Feature Extractionmentioning

confidence: 99%

Identify Lysine Neddylation Sites Using Bi-profile Bayes Feature Extraction via the Chou’s 5-steps Rule and General Pseudo Components

Wang

2020

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Introduction: Neddylation is a highly dynamic and reversible post-translatiNeddylation is a highly dynamic and reversible post-translational modification. The abnormality of neddylation has previously been shown to be closely related to some human diseases. The detection of neddylation sites is essential for elucidating the regulation mechanisms of protein neddylation.onal modification which has been found to be involved in various biological processes and closely associated with many diseases. The accurate identification of neddylation sites is necessary to elucidate the underlying molecular mechanisms of neddylation. As the traditional experimental methods are time consuming and expensive, it is desired to develop computational methods to predict neddylation sites. In this study, a novel predictor named NeddPred is proposed to predict lysine neddylation sites. An effective feature extraction method, bi-profile bayes encoding, is employed to encode neddylation sites. Moreover, a fuzzy support vector machine algorithm is proposed to solve the class imbalance and noise problem in the prediction of neddylation sites. As illustrated by 10-fold cross-validation, NeddPred achieves an excellent performance with a Matthew's correlation coefficient of 0.7082 and an area under receiver operating characteristic curve of 0.9769. Independent tests show that NeddPred significantly outperforms existing neddylation sites predictor NeddyPreddy. Therefore, NeddPred can be a complement to the existing tools for the prediction of neddylation sites. A user-friendly web-server for NeddPred is established at 123.206.31.171/NeddPred/. Objective: As the detection of the lysine neddylation sites by the traditional experimental method is often expensive and time-consuming, it is imperative to design computational methods to identify neddylation sites. Methods: In this study, a bioinformatics tool named NeddPred is developed to identify underlying protein neddylation sites. A bi-profile bayes feature extraction is used to encode neddylation sites and a fuzzy support vector machine model is utilized to overcome the problem of noise and class imbalance in the prediction. Results: Matthew's correlation coefficient of NeddPred achieved 0.7082 and an area under the receiver operating characteristic curve of 0.9769. Independent tests show that NeddPred significantly outperforms existing lysine neddylation sites predictor NeddyPreddy. Conclusion: Therefore, NeddPred can be a complement to the existing tools for the prediction of neddylation sites. A user-friendly webserver for NeddPred is accessible at 123.206.31.171/NeddPred/.

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“…Yang et al created a new data set with 304 sub-Golgi proteins for training and 64 sub-Golgi proteins for testing classification models [21]. Ahmad and Hayat [32] proposed a Golgi protein classification model using multivoting feature selection. Zhou [33] proposed XGBoost conditional covariance minimization based on multifeature fusion to predict Golgi protein types.…”

Section: Introductionmentioning

confidence: 99%

SubRF_Seq: Identification of Sub-Golgi Protein Types with Random Forest with Partial Sequence Information

Cui

Cao

Bao

et al. 2020

Scientific Programming

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In the recent years, the subject of Golgi classification has been studied intensively. It has been scientifically proven that Golgi can synthesize many substances, such as polysaccharides, and it can also combine proteins with sugars or lipids with glycoproteins and lipoproteins. In some cells (such as liver cells), the Golgi apparatus is also involved in the synthesis and secretion of lipoproteins. Therefore, the loss of Golgi protein function may have severe effects on the human body. For example, Alzheimer’s disease and diabetes are related to the loss of Golgi protein function. Because the classification of Golgi proteins has a specific effect on the treatment of these diseases, many scholars have studied the classification of Golgi proteins, but the data sets they used were complete Golgi sequences. The focus of this article is whether there is redundancy in the Golgi protein classification or, in other words, whether a part of the entire Golgi protein sequence can be used to complete the Golgi protein classification. Besides, we have adopted a new method to deal with the problem of sample imbalance. After experiments, our model has certain observability.

show abstract

MFSC: Multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou's PseAAC components

Cited by 48 publications

References 107 publications

Discrimination of Golgi Proteins Through Efficient Exploitation of Hybrid Feature Spaces Coupled With SMOTE and Ensemble of Support Vector Machine

Discrimination of Golgi Proteins Through Efficient Exploitation of Hybrid Feature Spaces Coupled With SMOTE and Ensemble of Support Vector Machine

Identify Lysine Neddylation Sites Using Bi-profile Bayes Feature Extraction via the Chou’s 5-steps Rule and General Pseudo Components

SubRF_Seq: Identification of Sub-Golgi Protein Types with Random Forest with Partial Sequence Information

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