Complex Network Study of the Immune Epitope Database for Parasitic Organisms

Vázquez-Prieto, Severo; Paniagua, E.; Solana, H.; Ubeira, Florencio M.

doi:10.2174/1568026618666171211150605

Cited by 3 publications

(1 citation statement)

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“…Peptides are in a balanced state in the human body, and play a crucial role in human physiology ( Vazquez-Prieto et al , 2017 ; Vázquez-Prieto et al , 2017 ). In addition, some therapeutic peptides play an important role in the field of biopharmaceutics.…”

Section: Introductionmentioning

confidence: 99%

PreTP-2L: identification of therapeutic peptides and their types using two-layer ensemble learning framework

2023

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Motivation Therapeutic peptides play an important role in immune regulation. Recently various therapeutic peptides have been used in the field of medical research, and have great potential in the design of therapeutic schedules. Therefore, it is essential to utilize the computational methods to predict the therapeutic peptides. However, the therapeutic peptides cannot be accurately predicted by the existing predictors. Furthermore, chaotic datasets are also an important obstacle of the development of this important field. Therefore, it is still challenging to develop a multi-classification model for identification of therapeutic peptides and their types. Results In this work, we constructed a general therapeutic peptide dataset. An ensemble learning method named PreTP-2L was developed for predicting various therapeutic peptide types. PreTP-2L consists of two layers. The first layer predicts whether a peptide sequence belongs to therapeutic peptide, and the second layer predicts if a therapeutic peptide belongs to a particular species. Availability A user-friendly webserver PreTP-2L can be accessed at http://bliulab.net/PreTP-2L . Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Introductionmentioning

confidence: 99%

PreTP-2L: identification of therapeutic peptides and their types using two-layer ensemble learning framework

2023

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PreTP-EL: prediction of therapeutic peptides based on ensemble learning

Guo

Yan

et al. 2021

Briefings in Bioinformatics

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Therapeutic peptides are important for understanding the correlation between peptides and their therapeutic diagnostic potential. The therapeutic peptides can be further divided into different types based on therapeutic function sharing different characteristics. Although some computational approaches have been proposed to predict different types of therapeutic peptides, they failed to accurately predict all types of therapeutic peptides. In this study, a predictor called PreTP-EL has been proposed via employing the ensemble learning approach to fuse the different features and machine learning techniques in order to capture the different characteristics of various therapeutic peptides. Experimental results showed that PreTP-EL outperformed other competing methods. Availability and implementation: A user-friendly web-server of PreTP-EL predictor is available at http://bliulab.net/PreTP-EL.

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PEPred-Suite: improved and robust prediction of therapeutic peptides using adaptive feature representation learning

et al. 2019

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Motivation Prediction of therapeutic peptides is critical for the discovery of novel and efficient peptide-based therapeutics. Computational methods, especially machine learning based methods, have been developed for addressing this need. However, most of existing methods are peptide-specific; currently, there is no generic predictor for multiple peptide types. Moreover, it is still challenging to extract informative feature representations from the perspective of primary sequences. Results In this study, we have developed PEPred-Suite, a bioinformatics tool for the generic prediction of therapeutic peptides. In PEPred-Suite, we introduce an adaptive feature representation strategy that can learn the most representative features for different peptide types. To be specific, we train diverse sequence-based feature descriptors, integrate the learnt class information into our features, and utilize a two-step feature optimization strategy based on the area under receiver operating characteristic curve to extract the most discriminative features. Using the learnt representative features, we trained eight random forest models for eight different types of functional peptides, respectively. Benchmarking results showed that as compared with existing predictors, PEPred-Suite achieves better and robust performance for different peptides. As far as we know, PEPred-Suite is currently the first tool that is capable of predicting so many peptide types simultaneously. In addition, our work demonstrates that the learnt features can reliably predict different peptides. Availability and implementation The user-friendly webserver implementing the proposed PEPred-Suite is freely accessible at http://server.malab.cn/PEPred-Suite. Supplementary information Supplementary data are available at Bioinformatics online.

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Complex Network Study of the Immune Epitope Database for Parasitic Organisms

Abstract: They confirm that this type of approach can be considered a valuable tool for preliminary screening of the best experimental conditions to determine whether the amino acid sequences being studied are true epitopes or not.

Cited by 3 publications

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PreTP-2L: identification of therapeutic peptides and their types using two-layer ensemble learning framework

PreTP-2L: identification of therapeutic peptides and their types using two-layer ensemble learning framework

PreTP-EL: prediction of therapeutic peptides based on ensemble learning

PEPred-Suite: improved and robust prediction of therapeutic peptides using adaptive feature representation learning

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