Predicting lysine lipoylation sites using bi-profile bayes feature extraction and fuzzy support vector machine algorithm

Ju, Zhe; Wang, Shiyun

doi:10.1016/j.ab.2018.09.007

Cited by 9 publications

(3 citation statements)

References 81 publications

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“…And the detailed information is shown in Tables S1-S6. It is shown that each sample can be calculated by the F-score with the BPB features [78,79], which can be demonstrated in Table 3. With the candidate lengths, we can find that the most available length is 23.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

LipoFNT: Lipoylation Sites Identification with Flexible Neural Tree

et al. 2019

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Lysine lipoylation is a special type of posttranslational modification in both prokaryotes' and eukaryotes' proteomics researches. Such a modification takes part in several significant biological processions and plays a key role in the cellular level. In order to construct and design an accurate classification algorithm for identifying lipoylation sites in the protein level, the computational approaches should be taken into account in this field. Meanwhile, several factors plays different role in the identification of modification sites. Considering such a situation, the foundational elements of the effective identification of modification sites are the available feature description and the high effective classification. With these two elements, the distinguishing between the lipoylation samples and the nonlipoylation samples can be treated as a typical classification issue in the field of machine learning. In this work, we have proposed a method named LipoFNT, which employed the two featuring sets, including the Position-Specific Scoring Matrix and bi-profile Bayesian, as the classification features. And then, the flexible neural tree algorithm is utilized to deal with the imbalance classification issue in lipoylation modification sample dataset. The proposed method can achieve 81.07% in sn%, 80.29% in sp, 80.68% in Acc, 0.8076 in F1, and 0.6136 in MCC, respectively. Meanwhile, we have demonstrated the relationship between the lengths of peptide and identification of modification sites.

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Section: Conclusion and Discussionmentioning

confidence: 99%

LipoFNT: Lipoylation Sites Identification with Flexible Neural Tree

et al. 2019

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“…From Tab. 9, we can find out the model comparison with amino acid composition (AAC) [36], KNN Features, Secondary tendency structure, Bigram [37], Tri gram [38], amino acid factors (AAF) [39], binary encoding (BE), bi-profile bays feature (BPB) [40] and flexible neural tree (LipoFNT) [41]. These methods are mostly used in computational biology.…”

Section: Comparison Analysis Of Our Proposed Methods With Other Feature Methodsmentioning

confidence: 99%

ILipo-PseAAC: Identification of Lipoylation Sites Using Statistical Moments and General PseAAC

Baig¹,

Khan²,

Alam

et al. 2022

Computers, Materials &Amp; Continua

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Lysine Lipoylation is a protective and conserved Post Translational Modification (PTM) in proteomics research like prokaryotes and eukaryotes. It is connected with many biological processes and closely linked with many metabolic diseases. To develop a perfect and accurate classification model for identifying lipoylation sites at the protein level, the computational methods and several other factors play a key role in this purpose. Usually, most of the techniques and different traditional experimental models have a very high cost. They are time-consuming; so, it is required to construct a predictor model to extract lysine lipoylation sites. This study proposes a model that could predict lysine lipoylation sites with the help of a classification method known as Artificial Neural Network (ANN). The ANN algorithm deals with the noise problem and imbalance classification in lipoylation sites dataset samples. As the result shows in ten-fold cross-validation, a brilliant performance is achieved through the predictor model with an accuracy of 99.88%, and also achieved 0.9976 as the highest value of MCC. So, the predictor model is a very useful and helpful tool for lipoylation sites prediction. Some of the residues around lysine lipoylation sites play a vital part in prediction, as demonstrated during feature analysis. The wonderful results reported through the evaluation and prediction of this model can provide an informative and relative explanation for lipoylation and its molecular mechanisms.

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“…If some of the documents in the k-nearest neighbour belong to the same class, the sum of the class weights of each neighbour in the classification is the similarity between the category and the test document. By sorting the candidate class scores and giving a threshold, the categories of test documents can be determined [27].…”

Section: Commonly Used Classification Methodsmentioning

confidence: 99%

Application of Feature Extraction Algorithm in the Construction of Interactive English Chinese Translation Mode

Dong

Yang

Zhang

2021

Wireless Communications and Mobile Computing

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In the process of English translation, traditional interactive English translation system is not obvious in English semantic context. The optimal feature selection process does not achieve the optimal translation solution, and the translation accuracy is low. Based on this, this paper designs an interactive English Chinese translation system based on a feature extraction algorithm. By introducing the feature extraction algorithm, the optimal translation solution is selected, and the semantic mapping model is constructed to translate the best translation into English Chinese translation. The real experiment results show that the interactive English Chinese translation system based on feature extraction algorithm can get the best solution.

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Predicting lysine lipoylation sites using bi-profile bayes feature extraction and fuzzy support vector machine algorithm

Cited by 9 publications

References 81 publications

LipoFNT: Lipoylation Sites Identification with Flexible Neural Tree

LipoFNT: Lipoylation Sites Identification with Flexible Neural Tree

ILipo-PseAAC: Identification of Lipoylation Sites Using Statistical Moments and General PseAAC

Application of Feature Extraction Algorithm in the Construction of Interactive English Chinese Translation Mode

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