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

Guo, Yichen; Yan, Ke; Lv, Hongwu; Liu, Bin

doi:10.1093/bib/bbab358

Cited by 43 publications

(20 citation statements)

References 43 publications

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“…Protein is the material basis of life, is an organic macromolecule, is the basic organic matter that constitutes cells, and is the main undertaker of life activities ( Wei et al, 2014 ; Wei et al, 2017 ; Guo et al, 2020 ; Tao et al, 2020 ; Wei et al, 2020 ; Guo et al, 2021 ). The human body contains many types of proteins with different properties and functions.…”

Section: Key Protein Identification Methodsmentioning

confidence: 99%

Research on the Computational Prediction of Essential Genes

Guo

Dong

et al. 2021

Front. Cell Dev. Biol.

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Genes, the nucleotide sequences that encode a polypeptide chain or functional RNA, are the basic genetic unit controlling biological traits. They are the guarantee of the basic structures and functions in organisms, and they store information related to biological factors and processes such as blood type, gestation, growth, and apoptosis. The environment and genetics jointly affect important physiological processes such as reproduction, cell division, and protein synthesis. Genes are related to a wide range of phenomena including growth, decline, illness, aging, and death. During the evolution of organisms, there is a class of genes that exist in a conserved form in multiple species. These genes are often located on the dominant strand of DNA and tend to have higher expression levels. The protein encoded by it usually either performs very important functions or is responsible for maintaining and repairing these essential functions. Such genes are called persistent genes. Among them, the irreplaceable part of the body’s life activities is the essential gene. For example, when starch is the only source of energy, the genes related to starch digestion are essential genes. Without them, the organism will die because it cannot obtain enough energy to maintain basic functions. The function of the proteins encoded by these genes is thought to be fundamental to life. Nowadays, DNA can be extracted from blood, saliva, or tissue cells for genetic testing, and detailed genetic information can be obtained using the most advanced scientific instruments and technologies. The information gained from genetic testing is useful to assess the potential risks of disease, and to help determine the prognosis and development of diseases. Such information is also useful for developing personalized medication and providing targeted health guidance to improve the quality of life. Therefore, it is of great theoretical and practical significance to identify important and essential genes. In this paper, the research status of essential genes and the essential genome database of bacteria are reviewed, the computational prediction method of essential genes based on communication coding theory is expounded, and the significance and practical application value of essential genes are discussed.

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Section: Key Protein Identification Methodsmentioning

confidence: 99%

Research on the Computational Prediction of Essential Genes

Guo

Dong

et al. 2021

Front. Cell Dev. Biol.

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“…Early methods like AntiInflam ( Gupta et al., 2017 ) and AIPpred ( Manavalan et al., 2018 ) only utilized a single algorithm. Recently, two excellent methods, PreTP-EL ( Guo et al., 2021 ) and PreTP-Stack ( Yan et al., 2022 ), were constructed by integrating several ML algorithms for therapeutic peptide prediction. Random forest (RF) ( Breiman, 2001 ) is the most popular algorithm; seven out of the eight methods employed it.…”

Section: Introductionmentioning

confidence: 99%

“…Second, three-quarters of existing methods only applied a single algorithm. However, lots of studies have proven that the ensemble learning model usually outperforms the single-algorithm-based model ( Guo et al., 2021 ; Jiang et al., 2021 ; Basith et al., 2022 ; Liang et al., 2021 ; Mishra et al., 2019 ). Thus, the utilization of an ensemble learning strategy might improve the performance of AIP identification.…”

Section: Introductionmentioning

confidence: 99%

Prediction of anti-inflammatory peptides by a sequence-based stacking ensemble model named AIPStack

Deng¹,

Lou²,

Wu³

et al. 2022

iScience

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“…Machine learning can identify desired proteins from a large number of sequences within a short time to guide the experimental discovery process ( Guo et al, 2020 ; Liu et al, 2020 ; Song G. et al, 2021 ; Cheng et al, 2021 ; Deng et al, 2021 ; Dong et al, 2021 ; Guo et al, 2021 ; Tang et al, 2021 ; Yu et al, 2021 ; Zhao et al, 2021 ). Over the past decades, researchers have developed many machine learning–based techniques for protein sequence analysis ( Zhai et al, 2020 ; Zeng et al, 2020 ; Chen et al, 2021 ; Li et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Immunoglobulin Classification Based on FC* and GC* Features

et al. 2022

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Immunoglobulins have a pivotal role in disease regulation. Therefore, it is vital to accurately identify immunoglobulins to develop new drugs and research related diseases. Compared with utilizing high-dimension features to identify immunoglobulins, this research aimed to examine a method to classify immunoglobulins and non-immunoglobulins using two features, FC* and GC*. Classification of 228 samples (109 immunoglobulin samples and 119 non-immunoglobulin samples) revealed that the overall accuracy was 80.7% in 10-fold cross-validation using the J48 classifier implemented in Weka software. The FC* feature identified in this study was found in the immunoglobulin subtype domain, which demonstrated that this extracted feature could represent functional and structural properties of immunoglobulins for forecasting.

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

Cited by 43 publications

References 43 publications

Research on the Computational Prediction of Essential Genes

Research on the Computational Prediction of Essential Genes

Prediction of anti-inflammatory peptides by a sequence-based stacking ensemble model named AIPStack

Immunoglobulin Classification Based on FC* and GC* Features

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