EPSOL: sequence-based protein solubility prediction using multidimensional embedding

Wu, Xiang; Yu, Liang

doi:10.1093/bioinformatics/btab463

Cited by 37 publications

(26 citation statements)

References 36 publications

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“…Bagging is one of the common ensemble learning models ( Dudoit and Fridlyand, 2003 ; Jin et al, 2019 ; Jin et al, 2021 ; Wu and Yu, 2021 ). The ensemble learning model uses a series of weak learners (also called basic models) for learning and integrates the results of each weak learner to obtain a better learning effect than individual learners.…”

Section: Methodsmentioning

confidence: 99%

DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins

et al. 2021

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Drug targets are biological macromolecules or biomolecule structures capable of specifically binding a therapeutic effect with a particular drug or regulating physiological functions. Due to the important value and role of drug targets in recent years, the prediction of potential drug targets has become a research hotspot. The key to the research and development of modern new drugs is first to identify potential drug targets. In this paper, a new predictor, DrugHybrid_BS, is developed based on hybrid features and Bagging-SVM to identify potentially druggable proteins. This method combines the three features of monoDiKGap (k = 2), cross-covariance, and grouped amino acid composition. It removes redundant features and analyses key features through MRMD and MRMD2.0. The cross-validation results show that 96.9944% of the potentially druggable proteins can be accurately identified, and the accuracy of the independent test set has reached 96.5665%. This all means that DrugHybrid_BS has the potential to become a useful predictive tool for druggable proteins. In addition, the hybrid key features can identify 80.0343% of the potentially druggable proteins combined with Bagging-SVM, which indicates the significance of this part of the features for research.

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Section: Methodsmentioning

confidence: 99%

DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins

et al. 2021

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“…AAC tried to count the composition information of peptides. In detail, AAC calculates the frequency of occurrence of each amino acid type ( Wei et al, 2018a ; Liu et al, 2019 ; Ning et al, 2020 ; Yang et al, 2020 ; Zhang and Zou, 2020 ; Wu and Yu, 2021 ). The computation formula of AAC is as follows:

where L denotes the length of the peptide, which is the number of characters in the peptide, AAC ( j ) denotes the percentage of amino acid j, N ( j ) denotes the total number of amino acid j .…”

Section: Methodsmentioning

confidence: 99%

iAIPs: Identifying Anti-Inflammatory Peptides Using Random Forest

Zhao

Teng

et al. 2021

Front. Genet.

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Recently, several anti-inflammatory peptides (AIPs) have been found in the process of the inflammatory response, and these peptides have been used to treat some inflammatory and autoimmune diseases. Therefore, identifying AIPs accurately from a given amino acid sequences is critical for the discovery of novel and efficient anti-inflammatory peptide-based therapeutics and the acceleration of their application in therapy. In this paper, a random forest-based model called iAIPs for identifying AIPs is proposed. First, the original samples were encoded with three feature extraction methods, including g-gap dipeptide composition (GDC), dipeptide deviation from the expected mean (DDE), and amino acid composition (AAC). Second, the optimal feature subset is generated by a two-step feature selection method, in which the feature is ranked by the analysis of variance (ANOVA) method, and the optimal feature subset is generated by the incremental feature selection strategy. Finally, the optimal feature subset is inputted into the random forest classifier, and the identification model is constructed. Experiment results showed that iAIPs achieved an AUC value of 0.822 on an independent test dataset, which indicated that our proposed model has better performance than the existing methods. Furthermore, the extraction of features for peptide sequences provides the basis for evolutionary analysis. The study of peptide identification is helpful to understand the diversity of species and analyze the evolutionary history of species.

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“…Low-rank sparse representation models have been applied in many fields ( Cheng et al, 2016 ; Chen et al, 2017 ; Zhang et al, 2017 ; Brbic and Kopriva, 2018 ; Chen et al, 2018 ; Xie et al, 2018 ; Yuanyuan et al, 2018 ; Zeng et al, 2018 ; Ding et al, 2019 ; Shen et al, 2019 ; Zhang et al, 2019 ; Li et al, 2020 ; Wu and Yu, 2021 ), which demonstrate high superiority, particularly in terms of dimensionality reduction and subspace segmentation. Considering existing analysis methods, introduce a low-rank sparse representation model for gene expression profile data analysis, several new methods for feature selection and feature extraction of gene expression profile data based on low-rank sparse representation models are explored, and they are applied to gene expression profile clustering and classification.…”

Section: Application Of Sparse Representation In Bioinformaticsmentioning

confidence: 99%

Application of Sparse Representation in Bioinformatics

Han

Wang²,

Guo

et al. 2021

Front. Genet.

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Inspired by L1-norm minimization methods, such as basis pursuit, compressed sensing, and Lasso feature selection, in recent years, sparse representation shows up as a novel and potent data processing method and displays powerful superiority. Researchers have not only extended the sparse representation of a signal to image presentation, but also applied the sparsity of vectors to that of matrices. Moreover, sparse representation has been applied to pattern recognition with good results. Because of its multiple advantages, such as insensitivity to noise, strong robustness, less sensitivity to selected features, and no “overfitting” phenomenon, the application of sparse representation in bioinformatics should be studied further. This article reviews the development of sparse representation, and explains its applications in bioinformatics, namely the use of low-rank representation matrices to identify and study cancer molecules, low-rank sparse representations to analyze and process gene expression profiles, and an introduction to related cancers and gene expression profile database.

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EPSOL: sequence-based protein solubility prediction using multidimensional embedding

Cited by 37 publications

References 36 publications

DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins

DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins

iAIPs: Identifying Anti-Inflammatory Peptides Using Random Forest

Application of Sparse Representation in Bioinformatics

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