Multi-view label embedding

Zhu, Pengfei; Hu, Qi; Hu, Qinghua; Zhang, Changqing; Feng, Zhizhao

doi:10.1016/j.patcog.2018.07.009

Cited by 52 publications

(23 citation statements)

References 14 publications

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“…Feature fusion can boost the recognition performance by combining the complementary information of different features (Zhu et al, 2016, 2018c). A 588-dimensional feature set was obtained by combining the features of the 188- and 400-dimensional feature sets, and a 661-dimensional feature set was obtained by combining the features of the 188- and 473-dimensional feature sets.…”

Section: Methodsmentioning

confidence: 99%

Identification of Phage Viral Proteins With Hybrid Sequence Features

Wang

2019

Front. Microbiol.

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The uniqueness of bacteriophages plays an important role in bioinformatics research. In real applications, the function of the bacteriophage virion proteins is the main area of interest. Therefore, it is very important to classify bacteriophage virion proteins and non-phage virion proteins accurately. Extracting comprehensive and effective sequence features from proteins plays a vital role in protein classification. In order to more fully represent protein information, this paper is more comprehensive and effective by combining the features extracted by the feature information representation algorithm based on sequence information (CCPA) and the feature representation algorithm based on sequence and structure information. After extracting features, the Max-Relevance-Max-Distance (MRMD) algorithm is used to select the optimal feature set with the strongest correlation between class labels and low redundancy between features. Given the randomness of the samples selected by the random forest classification algorithm and the randomness features for producing each node variable, a random forest method is employed to perform 10-fold cross-validation on the bacteriophage protein classification. The accuracy of this model is as high as 93.5% in the classification of phage proteins in this study. This study also found that, among the eight physicochemical properties considered, the charge property has the greatest impact on the classification of bacteriophage proteins These results indicate that the model discussed in this paper is an important tool in bacteriophage protein research.

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Section: Methodsmentioning

confidence: 99%

Identification of Phage Viral Proteins With Hybrid Sequence Features

Wang

2019

Front. Microbiol.

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“…As discussed in previous studies, feature extraction is a key step for constructing a computational predictor in bioinformatics. Primary structure of protein is represented as a sequence of letter; each of them denotes an amino acid residue.…”

Section: Methodsmentioning

confidence: 99%

“…SVM has been widely used in the bioinformatics fields for its excellent performance in classification . SVM transforms the data into a high‐dimensional space and then calculates the optimal separation of hyperplane among the data marked with different labels.…”

Section: Methodsmentioning

confidence: 99%

SecProMTB: Support Vector Machine‐Based Classifier for Secretory Proteins Using Imbalanced Data Sets Applied to Mycobacterium tuberculosis

2019

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Secretory proteins of Mycobacterium tuberculosis have created more concern, given their dominant immunogenicity and role in pathogenesis. In view of expensive and time‐consuming traditional biochemical experiments, an advanced support vector machine model named SecProMTB is constructed in this study and the proteins are identified by a bioinformatic approach. First, an improved pseudo‐amino acid composition (PseAAC) algorithm is used to extract features from all entities. Second, a novel imbalanced‐data strategy is proposed and adopted to divide the original data set into train set and test set. Third, to overcome the overfitting problem, feature‐ranking algorithms are applied with an increment feature selection. Finally, the model is trained and optimized. Consequently, a model is obtained with an area under the curve of 0.862 and average accuracy of 86% in the independent test. For the convenience of users, SecProMTB and related data are openly accessible at http://server.malab.cn/SecProMTB/index.jsp.

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“…RF is implemented by constructing a large number of decision trees during training and outputting the class pattern of individual trees [55]. The RF algorithm behaves similar to the ensemble algorithm [74,75]; it consists of decision trees, and each is grown according to a subset of features selected by the stochastic feature selection technique. The feature number of each tree is determined by a number of factors, including generalization errors, classifier strength, and dependence.…”

Section: ) Random Forestmentioning

confidence: 99%

Identifying G-protein Coupled Receptors Using Mixed-Feature Extraction Methods and Machine Learning Methods

Gao

Yu³

2024

IEEE Access

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G-protein-coupled receptors (GPCRs) are important protein molecules in the field of cell signaling and are widely found in various organisms. GPCRs play an important role in a variety of physiological processes and are important drug targets for a variety of diseases. Accurate prediction of GPCRs using machine learning is useful for drug design in a variety of related diseases. In this paper, we propose a method for identifying GPCRs based on mixed-feature vectors. We combine three individual features, such as 400D, N-gram and Parallel correlation pseudo amino acid composition (PC-PseAAC), using mixed-feature representation methods, which are evaluated by Random Forest, Naïve Bayes, and J48 for classification purposes. To measure the performance of this classifier, tenfold cross-validation is used. Two dimensionality reduction methods-the max-relevance-max-distance (MRMD) and t-Distributed Stochastic Neighbor Embedding (t-SNE)-are applied to reduce the feature dimension. The 400D and PC-PseAAC feature extraction methods are combined, the random forest is used as the classifier, and the area under the curve (AUC) is up to 0.9413. Therefore, among these methods, the new feature vector obtained by combining the two features shows the best performance, and the mixed feature is better than the single feature. INDEX TERMS G-protein-coupled receptors (GPCRs), J48, mixed-feature methods, naïve bayes, random forest. I. INTRODUCTION G protein-coupled receptors (GPCRs) are seven transmembrane proteins that perform reactions to transduce extracellular signals into cells. Because of their characteristic configuration of seven transmembrane alpha-helical counterclockwise beams [1], GPCRs are one of the largest membrane protein superfamilies, containing more than 800 genes in the human genome [2]. GPCRs are mainly divided into the following six classes [3]: rhodopsin-like receptors, secretin-like receptors, metabo-tropic glutamate receptors, fungal mating pheromone receptors, cyclic AMP (cAMP) receptors, and frizzled receptors. GPCRs bind a variety of ligands, such as small molecule organic compounds, eicosanoids, peptides, and proteins [2]. GPCRs play an important role in many basic physicochemical processes, such as human vision, taste, smell, metabolism, neurotransmission, immune regulation, and cell growth [4-9]. These basic physicochemical processes are carried out by binding of GPCRs with ligand to activate a guanine-binding protein (G protein). At present, most drugs target GPCRs [10, 11], and thus GPCRs are involved in various diseases including depression, diabetes, cancer, and central nervous system diseases are widely targeted in drug development. In the future, accurate prediction of GPCRs is of great significance for drug design for various related diseases. As the data available on GPCRs continues to increase, many methods for predicting GPCRs have been proposed. These methods mainly predict GPCRs from two aspects: one is based on statistical and machine learning algorithms, and the other is based on the extractio...

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Multi-view label embedding

Cited by 52 publications

References 14 publications

Identification of Phage Viral Proteins With Hybrid Sequence Features

Identification of Phage Viral Proteins With Hybrid Sequence Features

SecProMTB: Support Vector Machine‐Based Classifier for Secretory Proteins Using Imbalanced Data Sets Applied to Mycobacterium tuberculosis

Identifying G-protein Coupled Receptors Using Mixed-Feature Extraction Methods and Machine Learning Methods

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