Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses

Esmaeili, Maryam; Mohabatkar, Hassan; Mohsenzadeh, Sasan

doi:10.1016/j.jtbi.2009.11.016

Cited by 272 publications

(107 citation statements)

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“…To deal with this problem, the PseAAC (Pseudo Amino Acid Composition) was introduced [53][54][55]. Ever since the concept of pseudo amino acid composition or Chou's PseAAC [55][56][57][58] was proposed, it has been widely used in many biomedicine and drug development areas [59,60] as well as nearly all the areas of computational proteomics(see, e.g., [39,43,45,[61][62][63][64][65][66][67][68][69][70][71][72][73] and a long list of references cited in two review papers [74,75]). Encouraged by the successes of using PseAAC to deal with protein/peptide sequences, its idea and approach have been extended to deal with DNA/RNA sequences [76][77][78][79][80][81][82] in computational genomics via PseKNC (Pseudo K-tuple Nucleotide Composition) [83,84].…”

Section: Proteins Sample Formulationmentioning

confidence: 99%

“…Of these three, however, the jackknife test is deemed the least arbitrary that can always yield a unique outcome for a given benchmark dataset as elucidated in [32]. Accordingly, the jackknife test has been widely recognized and increasingly used by investigators to examine the quality of various predictors (see, e.g., [35,39,41,63,65,[102][103][104][105]). Accordingly, the jackknife test was also used in this study.…”

Section: Jackknife Testmentioning

confidence: 99%

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pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins

Xiao¹,

Cheng²,

Su³

et al. 2017

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The basic unit in life is cell. It contains many protein molecules located at its different organelles. The growth and reproduction of a cell as well as most of its other biological functions are performed via these proteins. But proteins in different organelles or subcellular locations have different functions. Facing the avalanche of protein sequences generated in the postgenomic age, we are challenged to develop high throughput tools for identifying the subcellular localization of proteins based on their sequence information alone. Although considerable efforts have been made in this regard, the problem is far apart from being solved yet. Most existing methods can be used to deal with single-location proteins only. Actually, proteins with multi-locations may have some special biological functions that are particularly important for drug targets. Using the ML-GKR (Multi-Label Gaussian Kernel Regression) method, we developed a new predictor called "pLoc-mGpos" by in-depth extracting the key information from GO (Gene Ontology) into the Chou's general PseAAC (Pseudo Amino Acid Composition) for predicting the subcellular localization of Gram-positive bacterial proteins with both single and multiple location sites. Rigorous cross-validation on a same stringent benchmark dataset indicated that the proposed pLoc-mGpos predictor is remarkably superior to "iLoc-Gpos", the state-of-the-art predictor for the same purpose. To maximize the convenience of most experimental scientists, a user-friendly web-server for the new powerful predictor has been established at Natural Science http://www.jci-bioinfo.cn/pLoc-mGpos/, by which users can easily get their desired results without the need to go through the complicated mathematics involved.

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Section: Proteins Sample Formulationmentioning

confidence: 99%

Section: Jackknife Testmentioning

confidence: 99%

pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins

Xiao¹,

Cheng²,

Su³

et al. 2017

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“…In statistical prediction, the following three cross-validation methods are often used to examine a predictor for its effectiveness in practical application: independent dataset test, subsampling test, and jackknife test (Chou and Zhang, 1995). However, as elucidated in Chou and Shen (2008) and demonstrated by Eqs.28-32 of Chou (2011), among the three cross-validation methods, the jackknife test is deemed the least arbitrary (most objective) that can always yield a unique result for a given benchmark dataset, and hence has been increasingly used and widely recognized by investigators to examine the accuracy of various predictors (Georgiou et al, 2009;Zeng et al, 2009;Esmaeili et al, 2010;Mohabatkar, 2010;Qiu et al, 2010;Hu et al, 2011aHu et al, , 2011bHuang et al, 2011aHuang et al, , 2011bLin et al, 2011;Wang et al, 2011;Xiao et al, 2011). Accordingly, the jackknife test, also known as Leave-One-Out Cross-Validation (LOOCV) (Huang et al, 2008;Cai et al, 2010;Huang et al, 2009Huang et al, , 2010aHuang et al, , 2010b) was adopted here to examine the quality of the present predictor.…”

Section: Predictor Construction and Evaluationmentioning

confidence: 99%

SySAP: a system-level predictor of deleterious single amino acid polymorphisms

et al. 2011

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Single amino acid polymorphisms (SAPs), also known as non-synonymous single nucleotide polymorphisms (nsSNPs), are responsible for most of human genetic diseases. Discriminate the deleterious SAPs from neutral ones can help identify the disease genes and understand the mechanism of diseases. In this work, a method of deleterious SAP prediction at system level was established. Unlike most existing methods, our method not only considers the sequence and structure information, but also the network information. The integration of network information can improve the performance of deleterious SAP prediction. To make our method available to the public, we developed SySAP (a System-level predictor of deleterious Single Amino acid Polymorphisms), an easy-to-use and high accurate web server. SySAP is freely available at http://www.biosino.org/ SySAP/and http://lifecenter.sgst.cn/SySAP/.

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“…Therefore, the jackknife test has been increasingly and widely adopted by investigators to test the power of various prediction methods (see, e.g. (Chen et al, 2009;Chou and Shen, 2007b;Chou and Shen, 2008b;Ding and Zhang, 2008;Esmaeili et al, 2010;He et al, 2010;Jiang et al, 2008;Lin, 2008;Lin et al, 2008;Qiu et al, 2009;Zeng et al, 2009;Zhou, 1998;Zhou et al, 2007)). However, to reduce the computational time, we adopted the 2-fold cross-validation in this study as done by many investigators with SVM as the prediction engine.…”

Section: Helicobacter Protein-protein Interaction (Hel)mentioning

confidence: 99%

High performance set of PseAAC and sequence based descriptors for protein classification

Nanni

Brahnam

Lumini

2010

Journal of Theoretical Biology

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This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Towards this end, we evaluate several feature extraction approaches for representing proteins starting from their amino acid sequence as well as different feature descriptor combinations using an ensemble of classifiers (support vector machines). In our experiments, more than ten different protein descriptors are compared using nine different datasets. We develop our system using a blind testing protocol, where the parameters of the system are optimized using one dataset and then validated using the other datasets (and so on for each dataset). Although different stand-alone classifiers work well on some datasets and not on others, we have discovered that fusion among 1 corresponding author: Tel.: +39 0547 339121; fax: +39 0547 338890.2 different methods obtains a good performance across all the tested datasets, especially when using the weighted sum rule.Included in our feature descriptor combinations is the introduction of two new descriptors, one based on wavelets and the other based on amino acid groups. Using our system, both outperform their standard implementations. We also consider as a baseline the simple amino acid composition (AC) and dipeptide composition (2G), since they have been widely used for protein classification.Our proposed method outperforms AC and 2G.

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Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses

Cited by 272 publications

References 69 publications

pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins

pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins

SySAP: a system-level predictor of deleterious single amino acid polymorphisms

High performance set of PseAAC and sequence based descriptors for protein classification

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