A novel method for protein secondary structure prediction using dual‐layer SVM and profiles

Guo, Jian; Hu, Changwei; Sun, Zhirong; Lin, You-Wei

doi:10.1002/prot.10634

Cited by 172 publications

(112 citation statements)

References 25 publications

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“…SVM was developed firstly to solve the classification problem, but it is also applied to the domain of regression problems. It becomes popular because of its success in many applications, such as handwriting recognition [7], image clustering [44], text categorization [40], gene classification [34], protein structure prediction [33], etc. hyperplanes, see [27].…”

Section: Multi-category Classification Svmmentioning

confidence: 99%

Filter-based multiscale entropy analysis of complex physiological time series

Zhao

2013

Phys. Rev. E

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Zhao, Liang, "Filter-based multiscale entropy analysis of complex physiological time series" (2013). Dissertations -ALL. Paper 3. ABSTRACTThe multiscale entropy (MSE) has been widely and successfully used in analyzing the complexity of physiologic time series. In this thesis, we re-interpret the averaging process in MSE as filtering a time series by a filter of a piecewise constant type. From this viewpoint, we introduce the filter-based multiscale entropy (FME) which filters a time series by filters to generate its multiple frequency components and then compute the blockwise entropy of the resulting components. By choosing filters adapted to the feature of a given time series, FME is able to better capture its multiscale information and to provide more flexibility for studying its complexity. Motivated by the heart rate turbulence theory which suggests that the human heartbeat interval time series (HHITS) can be described in piecewise linear patterns, we propose the piecewise linear filter multiscale entropy (PLFME) for the complexity analysis of the time series. Numerical results from PLFME are more robust to data of various lengths than those from MSE. We then propose wavelet packet transform entropy (WPTE) analysis. We apply WPTE analysis to HHITS using lower and higher piecewise linear filters. Numerical results show that WPTE using piecewise linear filters gives us the highest classification rates discriminating different cardiac systems among other multiscale entropy analysis. At the end, we discuss the application of FME on discrete time series. We introduce an 'eliminating' algorithm to examine and compare the complexity of coding and noncoding DNA sequences.

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Section: Multi-category Classification Svmmentioning

confidence: 99%

Filter-based multiscale entropy analysis of complex physiological time series

Zhao

2013

Phys. Rev. E

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“…a homotetramer) and those made up of different subunits are referred to with a prefix of "hetero-" (e.g. a heterotetramer, such as the two alpha and two beta chains of hemoglobin) [19]. …”

Section: Quaternary Structurementioning

confidence: 99%

Protein Structure Prediction Using Support Vector Machine

Mandle¹,

Jain²,

Shrivastava³

2012

IJSC

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“…[14,15] In this paper, we also adapt the PSSM profiles as our primal feature set instead of sequence residue type only. We obtain the PSSM profiles by using PSI-BLAST and non-redundant (NR) protein database.…”

Section: Pssm Profilesmentioning

confidence: 99%

Using Efficient RBF Network to Identify Interface Residues Based on PSSM Profiles and Biochemical Properties

Chen

Shao

et al. 2007

Pattern Recognition in Bioinformatics

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Abstract. Protein-protein interactions play a very important role in many biological processes, for example, information transfer along signaling pathways, and enzyme catalysis. Recently, scientists tried to predict the protein-protein interaction interface from sequences. Since the number of protein 3D structure still increase slowly comparing to the number of protein sequences, it may be a good idea to predict the protein-protein interface from sequences directly.In this paper, the compositions and conserved functions of the amino acids in the protein interface are studied, and the information of secondary structures is added. In addition, we used radio basis function network to predict the protein interface with adding some useful biochemical features.

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A novel method for protein secondary structure prediction using dual‐layer SVM and profiles

Cited by 172 publications

References 25 publications

Filter-based multiscale entropy analysis of complex physiological time series

Filter-based multiscale entropy analysis of complex physiological time series

Protein Structure Prediction Using Support Vector Machine

Using Efficient RBF Network to Identify Interface Residues Based on PSSM Profiles and Biochemical Properties

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