Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach

Zeng, Yuhong; Guo, Yanzhi; Xiao, Rong-quan; Yang, Li V.; Yu, Lezheng; Li, Menglong

doi:10.1016/j.jtbi.2009.03.028

Cited by 202 publications

(96 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In statistical prediction, three cross-validation methods are often used: subsampling test, independent dataset test, and jackknife test (Chou and Zhang, 1995). However, as demonstrated in (Chou and Shen, 2007), the jackknife test has the least arbitrariness and therefore has been increasingly and widely used to test various prediction methods (see, e.g., (Chen et al, 2008;Chen and Han, 2009;Chou and Shen, 2008a;Chou and Shen, 2008b;Chou and Shen, 2009;Ding et al, 2009a;Ding et al, 2009b;Du and Li, 2008;Georgiou et al, 2009;2008;Nanni and Lumini, 2009;Rezaei et al, 2008;Shi et al, 2008;Tian et al, 2008;Wang et al, 2008;Xiao et al, 2009b;Zeng et al, 2009;). In the jackknife or leave-one-out test each case in the database is predicted for the model constructed using all the cases except the one being predicted.…”

Section: Resultsmentioning

confidence: 99%

A network-QSAR model for prediction of genetic-component biomarkers in human colorectal cancer

Vilar

González-Dı́az

Santana

et al. 2009

Journal of Theoretical Biology

View full text Add to dashboard Cite

The combination of the network theory and the calculation of Topological Indices (TIs) allow establishing relationships between the molecular structure of large molecules like the genes and proteins and their properties at a biological level. This type of models can be considered Quantitative Structure-Activity Relationships (QSAR) for biopolymers. In the present work a QSAR model is reported for proteins, related to Human Colorectal Cancer (HCC) and codified by different genes that have been identified experimentally by Sjöblom et al.(Science, 314 (2006)

show abstract

Section: Resultsmentioning

confidence: 99%

A network-QSAR model for prediction of genetic-component biomarkers in human colorectal cancer

Vilar

González-Dı́az

Santana

et al. 2009

Journal of Theoretical Biology

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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/.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach

Cited by 202 publications

References 69 publications

A network-QSAR model for prediction of genetic-component biomarkers in human colorectal cancer

A network-QSAR model for prediction of genetic-component biomarkers in human colorectal cancer

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

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

Contact Info

Product

Resources

About