Prediction of drug-target interactions based on multi-layer network representation learning

Shang, Yifan; Gao, Lin; Zou, Quan; Yu, Liang

doi:10.1016/j.neucom.2020.12.068

Cited by 58 publications

(25 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Selecting the feature-encodings that are instructive and autonomous is an important step in creating machine learning models [37] , [38] , [39] , [40] . Expressing the protein sequences with a mathematical formulation is key and difficult in functional element identification [41] , [42] , [43] , [44] .…”

Section: Methodsmentioning

confidence: 99%

Identification of cyclin protein using gradient boost decision tree algorithm

Zulfiqar

Yuan

Huang

et al. 2021

Computational and Structural Biotechnology Journal

View full text Add to dashboard Cite

show abstract

Section: Methodsmentioning

confidence: 99%

Identification of cyclin protein using gradient boost decision tree algorithm

Zulfiqar

Yuan

Huang

et al. 2021

Computational and Structural Biotechnology Journal

View full text Add to dashboard Cite

show abstract

“…To date, there are approximately 130 protein families as therapeutic drug targets, which usually include enzymes ( Liu et al, 2019a ; Meng et al, 2020 ; Xu et al, 2021a ; Wang et al, 2021 ), G protein-coupled receptors ( Ru et al, 2020 ), ion channels and transporters ( Han et al, 2019 ), nuclear hormone receptors, etc ( Li and Lai, 2007 ). These drug targets are of great significance for disease treatment and drug research and development ( Ding et al, 2019a ; Ding et al, 2019b ; Shi et al, 2019 ; Ding et al, 2020a ; Wang et al, 2020a ; Ding et al, 2020b ; Shang et al, 2021 ; Zhuang et al, 2021 ). However, the discovery and development of modern drugs is usually a time-consuming and laborious process.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Several endogenous peptides found in the process of inflammatory response have become anti-inflammatory agents and can be used as new therapies for autoimmune diseases and inflammatory disorders ( Gonzalez-Rey et al, 2007 ; Yu et al, 2020a ). Compared with small-molecule drugs, the therapy based on peptides has minimal toxicity and high specificity under normal conditions, which is a better choice for inflammatory and autoimmune disorders and has been widely used in treatment ( de la Fuente-Núñez et al, 2017 ; Shang et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

iAIPs: Identifying Anti-Inflammatory Peptides Using Random Forest

Zhao

Teng

et al. 2021

Front. Genet.

View full text Add to dashboard Cite

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.

show abstract

Prediction of drug-target interactions based on multi-layer network representation learning

Cited by 58 publications

References 42 publications

Identification of cyclin protein using gradient boost decision tree algorithm

Identification of cyclin protein using gradient boost decision tree algorithm

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

iAIPs: Identifying Anti-Inflammatory Peptides Using Random Forest

Contact Info

Product

Resources

About