A study of entropy/clarity of genetic sequences using metric spaces and fuzzy sets

Georgiou, D.N.; Karakasidis, Theodoros E.; Nieto, Juan J.; Torres, Ángela

doi:10.1016/j.jtbi.2010.08.010

Cited by 26 publications

(17 citation statements)

References 113 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The principle of Zernike moments [60,61,62,63] is Zernike polynomials [64,65,66], that is a set of complete orthogonal polynomials within the unit circle. In two-dimensional space, these polynomials can be expressed as {

V_{n m} (x, y)

} and expression is as follows:

V_{n m} (x, y) = V_{n m} (ρ, θ) = R_{n m} (ρ) e^{j m θ} for ρ \leq 1

where n is a nonnegative integer and m is an integer subject to constraints n −| m | even, | m | ≤ n .…”

Section: Materials and Methodologymentioning

confidence: 99%

PCVMZM: Using the Probabilistic Classification Vector Machines Model Combined with a Zernike Moments Descriptor to Predict Protein–Protein Interactions from Protein Sequences

Wang

You

Xiao

et al. 2017

IJMS

View full text Add to dashboard Cite

Protein–protein interactions (PPIs) are essential for most living organisms’ process. Thus, detecting PPIs is extremely important to understand the molecular mechanisms of biological systems. Although many PPIs data have been generated by high-throughput technologies for a variety of organisms, the whole interatom is still far from complete. In addition, the high-throughput technologies for detecting PPIs has some unavoidable defects, including time consumption, high cost, and high error rate. In recent years, with the development of machine learning, computational methods have been broadly used to predict PPIs, and can achieve good prediction rate. In this paper, we present here PCVMZM, a computational method based on a Probabilistic Classification Vector Machines (PCVM) model and Zernike moments (ZM) descriptor for predicting the PPIs from protein amino acids sequences. Specifically, a Zernike moments (ZM) descriptor is used to extract protein evolutionary information from Position-Specific Scoring Matrix (PSSM) generated by Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then, PCVM classifier is used to infer the interactions among protein. When performed on PPIs datasets of Yeast and H. Pylori, the proposed method can achieve the average prediction accuracy of 94.48% and 91.25%, respectively. In order to further evaluate the performance of the proposed method, the state-of-the-art support vector machines (SVM) classifier is used and compares with the PCVM model. Experimental results on the Yeast dataset show that the performance of PCVM classifier is better than that of SVM classifier. The experimental results indicate that our proposed method is robust, powerful and feasible, which can be used as a helpful tool for proteomics research.

show abstract

V_{n m} (x, y)

} and expression is as follows:

V_{n m} (x, y) = V_{n m} (ρ, θ) = R_{n m} (ρ) e^{j m θ} for ρ \leq 1

where n is a nonnegative integer and m is an integer subject to constraints n −| m | even, | m | ≤ n .…”

Section: Materials and Methodologymentioning

confidence: 99%

PCVMZM: Using the Probabilistic Classification Vector Machines Model Combined with a Zernike Moments Descriptor to Predict Protein–Protein Interactions from Protein Sequences

Wang

You

Xiao

et al. 2017

IJMS

View full text Add to dashboard Cite

show abstract

“…PSSM is very useful to predict protein quaternary structural attributes, disulfide connectivity, and folding patterns . Thus, it is used to predict PPIs in this work.…”

Section: Materials and Methodologymentioning

confidence: 99%

Improving protein–protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model

Meng

You

et al. 2016

Protein Science

View full text Add to dashboard Cite

Predicting protein–protein interactions (PPIs) is a challenging task and essential to construct the protein interaction networks, which is important for facilitating our understanding of the mechanisms of biological systems. Although a number of high‐throughput technologies have been proposed to predict PPIs, there are unavoidable shortcomings, including high cost, time intensity, and inherently high false positive rates. For these reasons, many computational methods have been proposed for predicting PPIs. However, the problem is still far from being solved. In this article, we propose a novel computational method called RVM‐BiGP that combines the relevance vector machine (RVM) model and Bi‐gram Probabilities (BiGP) for PPIs detection from protein sequences. The major improvement includes (1) Protein sequences are represented using the Bi‐gram probabilities (BiGP) feature representation on a Position Specific Scoring Matrix (PSSM), in which the protein evolutionary information is contained; (2) For reducing the influence of noise, the Principal Component Analysis (PCA) method is used to reduce the dimension of BiGP vector; (3) The powerful and robust Relevance Vector Machine (RVM) algorithm is used for classification. Five‐fold cross‐validation experiments executed on yeast and Helicobacter pylori datasets, which achieved very high accuracies of 94.57 and 90.57%, respectively. Experimental results are significantly better than previous methods. To further evaluate the proposed method, we compare it with the state‐of‐the‐art support vector machine (SVM) classifier on the yeast dataset. The experimental results demonstrate that our RVM‐BiGP method is significantly better than the SVM‐based method. In addition, we achieved 97.15% accuracy on imbalance yeast dataset, which is higher than that of balance yeast dataset. The promising experimental results show the efficiency and robust of the proposed method, which can be an automatic decision support tool for future proteomics research. For facilitating extensive studies for future proteomics research, we developed a freely available web server called RVM‐BiGP‐PPIs in Hypertext Preprocessor (PHP) for predicting PPIs. The web server including source code and the datasets are available at http://219.219.62.123:8888/BiGP/.

show abstract

“…It emerged from a group of sequences formerly arranged by structural or similarity [34]. There are many methods of calculating distances and metric spaces [35,36]. Here, some research on PSSM methods and its relation to amino acids is discussed.…”

Section: Position-specific Scoring Matrixmentioning

confidence: 99%

An Ensemble Classifier with Random Projection for Predicting Protein–Protein Interactions Using Sequence and Evolutionary Information

et al. 2018

View full text Add to dashboard Cite

Identifying protein-protein interactions (PPIs) is crucial to comprehend various biological processes in cells. Although high-throughput techniques generate many PPI data for various species, they are only a petty minority of the entire PPI network. Furthermore, these approaches are costly and time-consuming and have a high error rate. Therefore, it is necessary to design computational methods for efficiently detecting PPIs. In this study, a random projection ensemble classifier (RPEC) was explored to identify novel PPIs using evolutionary information contained in protein amino acid sequences. The evolutionary information was obtained from a position-specific scoring matrix (PSSM) generated from PSI-BLAST. A novel feature fusion scheme was then developed by combining discrete cosine transform (DCT), fast Fourier transform (FFT), and singular value decomposition (SVD). Finally, via the random projection ensemble classifier, the performance of the presented approach was evaluated on Yeast, Human, and H. pylori PPI datasets using 5-fold cross-validation. Our approach achieved high prediction accuracies of 95.64%, 96.59%, and 87.62%, respectively, effectively outperforming other existing methods. Generally speaking, our approach is quite promising and supplies a practical and effective method for predicting novel PPIs.

show abstract

A study of entropy/clarity of genetic sequences using metric spaces and fuzzy sets

Cited by 26 publications

References 113 publications

PCVMZM: Using the Probabilistic Classification Vector Machines Model Combined with a Zernike Moments Descriptor to Predict Protein–Protein Interactions from Protein Sequences

PCVMZM: Using the Probabilistic Classification Vector Machines Model Combined with a Zernike Moments Descriptor to Predict Protein–Protein Interactions from Protein Sequences

Improving protein–protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model

An Ensemble Classifier with Random Projection for Predicting Protein–Protein Interactions Using Sequence and Evolutionary Information

Contact Info

Product

Resources

About