A Short Survey on Genetic Sequences, Chou’s Pseudo Amino Acid Composition and its Combination with Fuzzy Set Theory

Georgiou, D.N.; Karakasidis, Theodoros E.; Megaritis, A.C.

doi:10.2174/1875036201307010041

Cited by 58 publications

(24 citation statements)

References 120 publications

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“…The value of , determined as the inverse of the variability in the data (the more the variability in the data the less the value of ), appropriately affects the membership degree (13) in terms of robustness to outliers. Figure 2 shows different membership curves, for different values of , obtained with the AR-FCMdCExp model applied to a simulated dataset with two clusters with centers in 0.5 and 0.6.…”

Section: A Robust Fuzzy Clustering Model For Time Seriesmentioning

confidence: 99%

“…ACCEPTED MANUSCRIPT 13  Computational complexity: the proposed AR-FCMdC-Exp algorithm could be very computationally intensive with large sample, since it is based on an exhaustive search for the medoids. Following Krishnaputan et al [82], a "linearized" version of the AR-FCMdC-Exp algorithm, Lin-AR-FCMdCExp, can be introduced to cope with this issue.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…Note that with , where denotes the identity matrix, we obtain the clustering model (12) and the membership degrees (13). In the following we set .…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…For detailed discussions on the benefits connected to autoregressive model-based clustering approach, fuzzy methodology, partitioning around medoids procedure see D'Urso et al [2,3]. We observe that the fuzzy approach has been employed for clustering/classification of sequences in other research areas, for instance in computational biology [4][5][6][7][8][9][10][11][12][13][14][15]. Following the guidelines proposed in [16][17][18][19][20][21][22][23], we will document the proposed algorithm for clustering time series according to the following procedures in order to make its development clearer and more useful: (i) select the information to be extracted by time series; (ii) select a proper distance measure for comparing time series; (iii) introduce or develop a powerful algorithm to operate the clustering; (iv) properly evaluate the accuracy of the clustering model; (iv) establish resources for the development of the algorithm that are publicly available.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Time series clustering by a robust autoregressive metric with application to air pollution

D’Urso

Giovanni

Massari

2015

Chemometrics and Intelligent Laboratory Systems

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In this paper, following a fuzzy approach and adopting an autoregressive parameterization, we propose a robust clustering model for classifying time series. In particular, by adopting a fuzzy\ud partitioning around medoids approach, the suggested clustering model is able to define the so-called medoid time series, which is a representative time series of each cluster, and the membership degrees of each time series to the different clusters. The robustness of the proposed clustering model is guaranteed by the adoption of a suitable robust metric for time series, i.e. the so-called exponential distance measure. In this way, the clustering model is able to tolerate the presence of outlier time series in the clustering process. In particular, it is capable of neutralizing and smoothing the disruptive effect of outlier time series, preserving the original clustering structure of the dataset, by assigning to outlier time series approximately the same membership degrees across clusters. To illustrate the usefulness and effectiveness of the suggested time series clustering model, a simulation study and an application to air pollution time series are carried out. Comparison with some existing clustering procedures suggested in the literature shows several advantages of the proposed model

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Section: A Robust Fuzzy Clustering Model For Time Seriesmentioning

confidence: 99%

Section: Accepted Manuscriptmentioning

confidence: 99%

“…Note that with , where denotes the identity matrix, we obtain the clustering model (12) and the membership degrees (13). In the following we set .…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Time series clustering by a robust autoregressive metric with application to air pollution

D’Urso

Giovanni

Massari

2015

Chemometrics and Intelligent Laboratory Systems

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show abstract

“…However, as elucidated in [38] and demonstrated by Eqs. (28)-(32) of [38], 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 (see, e.g., [46,[52][53][54][55][61][62][63][64][65][66][67][68][69][70]). Accordingly, the jackknife test is also adopted here to examine the quality of the present predictor.…”

Section: Prediction Assessmentmentioning

confidence: 99%

Accurate prediction of protein structural classes by incorporating PSSS and PSSM into Chou's general PseAAC

Zhang

2015

Chemometrics and Intelligent Laboratory Systems

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PseDNA‐Pro: DNA‐Binding Protein Identification by Combining Chou’s PseAAC and Physicochemical Distance Transformation

Liu

Fan

et al. 2014

Molecular Informatics

156

113

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Identification of DNA-binding proteins is an important problem in biomedical research as DNA-binding proteins are crucial for various cellular processes. Currently, the machine learning methods achieve the-state-of-the-art performance with different features. A key step to improve the performance of these methods is to find a suitable representation of proteins. In this study, we proposed a feature vector composed of three kinds of sequence-based features, including overall amino acid composition, pseudo amino acid composition (PseAAC) proposed by Chou and physicochemical distance transformation. These features not only consider the sequence composition of proteins, but also incorporate the sequence-order information of amino acids in proteins. The feature vectors were fed into Support Vector Machine (SVM) for DNA-binding protein identification. The proposed method is called PseDNA-Pro. Experiments on stringent benchmark datasets and independent test datasets by using the Jackknife test showed that PseDNA-Pro can achieve an accuracy of higher than 80 %, outperforming several state-of-the-art methods, including DNAbinder, DNA-Prot, and iDNA-Prot. These results indicate that the combination of various features for DNA-binding protein prediction is a suitable approach, and the sequence-order information among residues in proteins is relative for discrimination. For practical applications, a web-server of PseDNA-Pro was established, which is available from http://bioinformatics.hitsz.edu.cn/PseDNA-Pro/.

show abstract

A Short Survey on Genetic Sequences, Chou’s Pseudo Amino Acid Composition and its Combination with Fuzzy Set Theory

Cited by 58 publications

References 120 publications

Time series clustering by a robust autoregressive metric with application to air pollution

Time series clustering by a robust autoregressive metric with application to air pollution

Accurate prediction of protein structural classes by incorporating PSSS and PSSM into Chou's general PseAAC

PseDNA‐Pro: DNA‐Binding Protein Identification by Combining Chou’s PseAAC and Physicochemical Distance Transformation

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