PUEPro: A Computational Pipeline for Prediction of Urine Excretory Proteins

Wang, Yan; Du, Wei; Liang, Yanchun; Chen, Xin; Zhang, Chi; Pang, Wei; Xu, Ying

doi:10.1007/978-3-319-49586-6_51

Cited by 9 publications

(17 citation statements)

References 30 publications

(39 reference statements)

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“…Charge is a factor for determining which proteins can be filtered through the glomerular membrane. Wang et al found that transmembrane domains and the radius of gyration are also prominent features of urine excretory proteins [24]. Published studies have observed that proteins with a radius smaller than 1.8 nm can pass through the GBM-slit diaphragm barrier, whereas proteins with a radius larger than 4.0 nm are retained.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, developing a computational method for accurately predicting urine excretory proteins could solve these problems to a certain extent. However, there is very little research focused on building a computational model for predicting urine excretory proteins [3,24]. The existing methods can be carried out in two main steps: (1) selecting features from constructed feature sets; and (2) applying a machine learning algorithm for training by using the selected features.…”

Section: Introductionmentioning

confidence: 99%

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DeepUEP: Prediction of Urine Excretory Proteins Using Deep Learning

Pang

et al. 2020

IEEE Access

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Urine excretory proteins are among the most commonly used biomarkers in body fluids. Computational identification of urine excretory proteins can provide very useful information for identifying targeted disease biomarkers in urine by linking transcriptome or proteomics data. There are few methods based on conventional machine learning algorithms for predicting urine excretory proteins, and most of these methods strongly depend on the extraction of features from urine excretory proteins. An end-to-end model for urine excretory protein prediction, called DeepUEP, is presented using deep neural networks relying on only amino acid sequence information. The model achieves good performance and outperforms existing methods on training and testing sets. By comparing known urinary protein biomarkers with the results of the model, we find that the model can achieve a true-positive rate of over 80% for urinary protein biomarkers that have been detected in more than one study. We also combine our model with transcriptome and proteomics data from lung cancer patients to predict the potential urinary protein biomarkers of lung cancer. A web server is developed for the prediction of urine excretory proteins, and it can be accessed at the following URL: http://www.csbg-jlu.info/DeepUEP/. We believe that our prediction model and web server are useful for biomedical researchers who are interested in identifying urinary protein biomarkers, especially for candidate proteins in transcriptome or proteomics analyses of diseased tissues.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DeepUEP: Prediction of Urine Excretory Proteins Using Deep Learning

Pang

et al. 2020

IEEE Access

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“…Survival analysis analyzed the relationship between genes and patient survival, which was carried out through the platform R2 (R2: Genomics Analysis and Visualization Platform) [20]. The predictive analysis of urinary protein excretion was analyzed by website developed by Wang and Du [19].…”

Section: Gene List Analysismentioning

confidence: 99%

“…As described in the introduction, serum and urine tumor markers are important for tumor diagnosis, which prompts us to predict and analyze these genes. Table 2 shows the predicted results of urinary excretion of proteins carried out by the website developed, by Wang and Du [19]. The top seven differentially expressed genes in pancreatic cancer were analyzed to predict whether their protein products were urinary secretory proteins.…”

Section: Prediction Of Urinary Excretory Proteinmentioning

confidence: 99%

“…To further understand the useful information of the genes obtained, we analyzed the pathways of the top 200 genes with the help of DAVID [17] and Cystoscope [18]. Through the algorithm proposed by Wang and Du [19] and the R2: Genomics Analysis and Visualization Platform [20], we proved that the genes selected were associated with the survival rate of patients with pancreatic cancer and their encoded proteins may enter urine and become useful urine markers. As a result, seven differentially expressed genes were predicted as specific biomarkers for pancreatic cancer because of their highest accuracy of classification on cancer and normal samples.…”

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Pancreatic Cancer Biomarker Detection By Two Support Vector Strategies for Recursive Feature Elimination

Wang

Liu

et al. 2019

Biomark. Med.

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Pancreatic cancer is one of the worst malignant tumors in prognosis. Therefore, to reduce the mortality rate of pancreatic cancer, early diagnosis and prompt treatment are particularly important. Results: We put forward a new feature-selection method that was used to find clinical markers for pancreatic cancer by combination of Support Vector Machine Recursive Feature Elimination (SVM-RFE) and Large Margin Distribution Machine Recursive Feature Elimination (LDM-RFE) algorithms. As a result, seven differentially expressed genes were predicted as specific biomarkers for pancreatic cancer because of their highest accuracy of classification on cancer and normal samples. Conclusion: Three (MMP7, FOS and A2M) out of the seven predicted gene markers were found to encode proteins secreted into urine, providing potential diagnostic evidences for pancreatic cancer.

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A multi-task positive-unlabeled learning framework to predict secreted proteins in human body fluids

He,

Wang,

Xie

et al. 2023

Complex Intell. Syst.

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Body fluid biomarkers are very important, because they can be detected in a non-invasive or minimally invasive way. The discovery of secreted proteins in human body fluids is an essential step toward proteomic biomarker identification for human diseases. Recently, many computational methods have been proposed to predict secreted proteins and achieved some success. However, most of them are based on a manual negative dataset, which is usually biased and therefore limits the prediction performances. In this paper, we first propose a novel positive-unlabeled learning framework to predict secreted proteins in a single body fluid. The secreted protein discovery in a single body fluid is transformed into multiple binary classifications and solved via multi-task learning. Also, an effective convolutional neural network is employed to reduce the overfitting problem. After that, we then improve this framework to predict secreted proteins in multiple body fluids simultaneously. The improved framework adopts a globally shared network to further improve the prediction performances of all body fluids. The improved framework was trained and evaluated on datasets of 17 body fluids, and the average benchmarks of 17 body fluids achieved an accuracy of 89.48%, F1 score of 56.17%, and PRAUC of 58.93%. The comparative results demonstrate that the improved framework performs much better than other state-of-the-art methods in secreted protein discovery.

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PUEPro: A Computational Pipeline for Prediction of Urine Excretory Proteins

Cited by 9 publications

References 30 publications

DeepUEP: Prediction of Urine Excretory Proteins Using Deep Learning

DeepUEP: Prediction of Urine Excretory Proteins Using Deep Learning

Pancreatic Cancer Biomarker Detection By Two Support Vector Strategies for Recursive Feature Elimination

A multi-task positive-unlabeled learning framework to predict secreted proteins in human body fluids

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