Discrimination of Thermophilic Proteins and Non-thermophilic Proteins Using Feature Dimension Reduction

Guo, Zifan; Wang, Pingping; Liu, Zhendong; Zhao, Yuming

doi:10.3389/fbioe.2020.584807

Cited by 46 publications

(27 citation statements)

References 96 publications

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“…It is very important to select good feature information for protein recognition ( Zuo et al, 2017 ; Zheng et al, 2019 ; Tang et al, 2020a ; Guo et al, 2020 ; Zhang et al, 2021 ). We chose the method based on PSSM profiles to extract the feature information of protein sequence data.…”

Section: Methodsmentioning

confidence: 99%

SNAREs-SAP: SNARE Proteins Identification With PSSM Profiles

et al. 2021

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Soluble N-ethylmaleimide sensitive factor activating protein receptor (SNARE) proteins are a large family of transmembrane proteins located in organelles and vesicles. The important roles of SNARE proteins include initiating the vesicle fusion process and activating and fusing proteins as they undergo exocytosis activity, and SNARE proteins are also vital for the transport regulation of membrane proteins and non-regulatory vesicles. Therefore, there is great significance in establishing a method to efficiently identify SNARE proteins. However, the identification accuracy of the existing methods such as SNARE CNN is not satisfied. In our study, we developed a method based on a support vector machine (SVM) that can effectively recognize SNARE proteins. We used the position-specific scoring matrix (PSSM) method to extract features of SNARE protein sequences, used the support vector machine recursive elimination correlation bias reduction (SVM-RFE-CBR) algorithm to rank the importance of features, and then screened out the optimal subset of feature data based on the sorted results. We input the feature data into the model when building the model, used 10-fold crossing validation for training, and tested model performance by using an independent dataset. In independent tests, the ability of our method to identify SNARE proteins achieved a sensitivity of 68%, specificity of 94%, accuracy of 92%, area under the curve (AUC) of 84%, and Matthew’s correlation coefficient (MCC) of 0.48. The results of the experiment show that the common evaluation indicators of our method are excellent, indicating that our method performs better than other existing classification methods in identifying SNARE proteins.

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Section: Methodsmentioning

confidence: 99%

SNAREs-SAP: SNARE Proteins Identification With PSSM Profiles

et al. 2021

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“…Feature redundancy or dimensionality disasters often occur during feature extraction. Feature selection not only reduces the risk of overfitting but also improves the model’s generalization ability and computational efficiency ( Guo et al, 2020 ; Yang et al, 2021a ; Ao et al, 2021b ; Zhao et al, 2021 ). In the present paper, we use the max relevance max distance (MRMD) feature selection method to reduce the dimensions of the initial feature set ( He et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest

2021

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DNA-binding protein (DBP) is a protein with a special DNA binding domain that is associated with many important molecular biological mechanisms. Rapid development of computational methods has made it possible to predict DBP on a large scale; however, existing methods do not fully integrate DBP-related features, resulting in rough prediction results. In this article, we develop a DNA-binding protein identification method called KK-DBP. To improve prediction accuracy, we propose a feature extraction method that fuses multiple PSSM features. The experimental results show a prediction accuracy on the independent test dataset PDB186 of 81.22%, which is the highest of all existing methods.

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“…Deep learning has shown impressive performance in many tasks ( Jiang et al, 2013 ; Guo et al, 2020 ; Jin et al, 2020 ; Tao et al, 2020 ; Yu et al, 2020b ; Zhang et al, 2020b ; Zhao et al, 2020 ; Jin et al, 2021 ; Liu et al, 2021b ; Lv et al, 2021 ; Su et al, 2021 ; Wang et al, 2021a ; Xu et al, 2021 ; Yu et al, 2021 ). This deep model-based strategy intends to build a deep feed-forward network and drug fingerprint encoding method to obtain the disease cell lines and drug quantitative characteristics.…”

Section: Methods Of Research On Drug Sensitivitymentioning

confidence: 99%

Bioinformatics Research on Drug Sensitivity Prediction

Chen

Juan

Lv³

et al. 2021

Front. Pharmacol.

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Modeling-based anti-cancer drug sensitivity prediction has been extensively studied in recent years. While most drug sensitivity prediction models only use gene expression data, the remarkable impacts of gene mutation, methylation, and copy number variation on drug sensitivity are neglected. Drug sensitivity prediction can both help protect patients from some adverse drug reactions and improve the efficacy of treatment. Genomics data are extremely useful for drug sensitivity prediction task. This article reviews the role of drug sensitivity prediction, describes a variety of methods for predicting drug sensitivity. Moreover, the research significance of drug sensitivity prediction, as well as existing problems are well discussed.

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Discrimination of Thermophilic Proteins and Non-thermophilic Proteins Using Feature Dimension Reduction

Cited by 46 publications

References 96 publications

SNAREs-SAP: SNARE Proteins Identification With PSSM Profiles

SNAREs-SAP: SNARE Proteins Identification With PSSM Profiles

KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest

Bioinformatics Research on Drug Sensitivity Prediction

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