Wavelet images and Chou’s pseudo amino acid composition for protein classification

Nanni, Loris; Brahnam, Sheryl; Lumini, Alessandra

doi:10.1007/s00726-011-1114-9

Cited by 123 publications

(56 citation statements)

References 76 publications

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“…In [34] a representation matrix based on physicochemical properties is proposed. First the physicochemical property response matrix PRM d ( i , j ) ∈ R N × N is obtained for a given protein P = ( p 1 , p 2 ,…, p N ) by selecting a physicochemical property d and setting the value of the element PRM d ( i , j ) to the sum of the value of the physicochemical property d of the amino-acid in position i of the protein and the value of the physicochemical property of the amino-acid in position j .…”

Section: Protein Representation Approachesmentioning

confidence: 99%

“…First [38] then later [34] have suggested using wavelets as a method to represents proteins. Since wavelet encoding requires a numerical representation, the protein sequence is first numerically encoded by substituting each amino acid with a value of a given physicochemical property d .…”

Section: Protein Representation Approachesmentioning

confidence: 99%

“…A sequence descriptor based on biorthogonal discrete wavelet is proposed in [34]. Given a protein P = ( p 1 , p 2 ,…, p N ) and a fixed physicochemical property d , each amino acid of the sequence is substituted by its value of d :

\begin{matrix} {PP}^{d} (i) = index (p_{i}, d), i \in [1, \dots, N], \end{matrix}

where the function index( i , d ) returns the value of the property d for the amino acid i .…”

Section: Protein Feature Extraction Approachesmentioning

confidence: 99%

“…The present paper provides an in-depth look at the protein representations that have led to the evolution of some of our previous work in this area.Reference [12], where an ensemble of approaches based on the amino-acid sequence was proposed.Reference [34], where several feature extraction methods based on the calculation of texture descriptors starting from a wavelet representation of the protein were proposed.Reference [28], where several feature extraction methods based on the calculation of PSSM were compared. …”

Section: Introductionmentioning

confidence: 99%

“…Reference [34], where several feature extraction methods based on the calculation of texture descriptors starting from a wavelet representation of the protein were proposed.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An Empirical Study of Different Approaches for Protein Classification

Nanni

Lumini

Brahnam

2014

The Scientific World Journal

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Many domains would benefit from reliable and efficient systems for automatic protein classification. An area of particular interest in recent studies on automatic protein classification is the exploration of new methods for extracting features from a protein that work well for specific problems. These methods, however, are not generalizable and have proven useful in only a few domains. Our goal is to evaluate several feature extraction approaches for representing proteins by testing them across multiple datasets. Different types of protein representations are evaluated: those starting from the position specific scoring matrix of the proteins (PSSM), those derived from the amino-acid sequence, two matrix representations, and features taken from the 3D tertiary structure of the protein. We also test new variants of proteins descriptors. We develop our system experimentally by comparing and combining different descriptors taken from the protein representations. Each descriptor is used to train a separate support vector machine (SVM), and the results are combined by sum rule. Some stand-alone descriptors work well on some datasets but not on others. Through fusion, the different descriptors provide a performance that works well across all tested datasets, in some cases performing better than the state-of-the-art.

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Section: Protein Representation Approachesmentioning

confidence: 99%

Section: Protein Representation Approachesmentioning

confidence: 99%

\begin{matrix} {PP}^{d} (i) = index (p_{i}, d), i \in [1, \dots, N], \end{matrix}

where the function index( i , d ) returns the value of the property d for the amino acid i .…”

Section: Protein Feature Extraction Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Reference [34], where several feature extraction methods based on the calculation of texture descriptors starting from a wavelet representation of the protein were proposed.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Empirical Study of Different Approaches for Protein Classification

Nanni

Lumini

Brahnam

2014

The Scientific World Journal

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IdentifyingDNA‐binding proteins based on multi‐features andLASSOfeature selection

Zhang

Zhu

et al. 2021

Biopolymers

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DNA‐binding proteins perform an indispensable function in the maintenance and processing of genetic information and are inefficiently identified by traditional experimental methods due to their huge quantities. On the contrary, machine learning methods as an emerging technique demonstrate satisfactory speed and accuracy when used to study these molecules. This work focuses on extracting four different features from primary and secondary sequence features: Reduced sequence and index‐vectors (RS), Pseudo‐amino acid components (PseAACS), Position‐specific scoring matrix‐Auto Cross Covariance Transform (PSSM‐ACCT), and Position‐specific scoring matrix‐Discrete Wavelet Transform (PSSM‐DWT). Using the LASSO dimension reduction method, we experiment on the combination of feature submodels to obtain the optimized number of top rank features. These features are respectively input into the training Ensemble subspace discriminant, Ensemble bagged tree and KNN to predict the DNA‐binding proteins. Three different datasets, PDB594, PDB1075, and PDB186, are adopted to evaluate the performance of the as‐proposed approach in this work. The PDB1075 and PDB594 datasets are adopted for the five‐fold cross‐validation, and the PDB186 is used for the independent experiment. In the five‐fold cross‐validation, both the PDB1075 and PDB594 show extremely high accuracy, reaching 86.98% and 88.9% by Ensemble subspace discriminant, respectively. The accuracy of independent experiment by multi‐classifiers voting is 83.33%, which suggests that the methodology proposed in this work is capable of predicting DNA‐binding proteins effectively.

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RF‐SVM: Identification ofDNA‐binding proteins based on comprehensive feature representation methods and support vector machine

Zhang

Gao

2021

Proteins

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Protein-DNA interactions play an important role in biological progress, such as DNA replication, repair, and modification processes. In order to have a better understanding of its functions, the one of the most important steps is the identification of DNAbinding proteins. We propose a DNA-binding protein predictor, namely, RF-SVM, which contains four types features, that is, pseudo amino acid composition (PseAAC), amino acid distribution (AAD), adjacent amino acid composition frequency (ACF) and Local-DPP. Random Forest algorithm is utilized for selecting top 174 features, which are established the predictor model with the support vector machine (SVM) on training dataset UniSwiss-Tr. Finally, RF-SVM method is compared with other existing methods on test dataset UniSwiss-Tst. The experimental results demonstrated that RF-SVM has accuracy of 84.25%. Meanwhile, we discover that the physicochemical properties of amino acids for OOBM770101(H), CIDH920104(H), MIYS990104(H), NISK860101(H), VINM940103(H), and SNEP660101(A) have contribution to predict DNA-binding proteins. The main code and datasets can gain in https://github.com/ NiJianWei996/RF-SVM.

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Wavelet images and Chou’s pseudo amino acid composition for protein classification

Cited by 123 publications

References 76 publications

An Empirical Study of Different Approaches for Protein Classification

An Empirical Study of Different Approaches for Protein Classification

IdentifyingDNA‐binding proteins based on multi‐features andLASSOfeature selection

RF‐SVM: Identification ofDNA‐binding proteins based on comprehensive feature representation methods and support vector machine

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