Prediction of Proteins in Cerebrospinal Fluid and Application to Glioma Biomarker Identification
Cerebrospinal fluid (CSF) proteins are very important because they can serve as biomarkers for central nervous system diseases. Although many CSF proteins have been identified with wet experiments, the identification of CSF proteins is still a challenge. In this paper, we propose a novel method to p...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/28/8/3617 |
| _version_ | 1827744236180602880 |
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| author | Kai He Yan Wang Xuping Xie Dan Shao |
| author_facet | Kai He Yan Wang Xuping Xie Dan Shao |
| author_sort | Kai He |
| collection | DOAJ |
| description | Cerebrospinal fluid (CSF) proteins are very important because they can serve as biomarkers for central nervous system diseases. Although many CSF proteins have been identified with wet experiments, the identification of CSF proteins is still a challenge. In this paper, we propose a novel method to predict proteins in CSF based on protein features. A two-stage feature-selection method is employed to remove irrelevant features and redundant features. The deep neural network and bagging method are used to construct the model for the prediction of CSF proteins. The experiment results on the independent testing dataset demonstrate that our method performs better than other methods in the prediction of CSF proteins. Furthermore, our method is also applied to the identification of glioma biomarkers. A differentially expressed gene analysis is performed on the glioma data. After combining the analysis results with the prediction results of our model, the biomarkers of glioma are identified successfully. |
| first_indexed | 2024-03-11T04:41:49Z |
| format | Article |
| id | doaj.art-ad388c2142234f6b9c5476a451c0d7e6 |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-11T04:41:49Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-ad388c2142234f6b9c5476a451c0d7e62023-11-17T20:41:33ZengMDPI AGMolecules1420-30492023-04-01288361710.3390/molecules28083617Prediction of Proteins in Cerebrospinal Fluid and Application to Glioma Biomarker IdentificationKai He0Yan Wang1Xuping Xie2Dan Shao3Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, ChinaKey Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, ChinaKey Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Computer Science and Technology, Changchun University, Changchun 130022, ChinaCerebrospinal fluid (CSF) proteins are very important because they can serve as biomarkers for central nervous system diseases. Although many CSF proteins have been identified with wet experiments, the identification of CSF proteins is still a challenge. In this paper, we propose a novel method to predict proteins in CSF based on protein features. A two-stage feature-selection method is employed to remove irrelevant features and redundant features. The deep neural network and bagging method are used to construct the model for the prediction of CSF proteins. The experiment results on the independent testing dataset demonstrate that our method performs better than other methods in the prediction of CSF proteins. Furthermore, our method is also applied to the identification of glioma biomarkers. A differentially expressed gene analysis is performed on the glioma data. After combining the analysis results with the prediction results of our model, the biomarkers of glioma are identified successfully.https://www.mdpi.com/1420-3049/28/8/3617cerebrospinal fluidglioma biomarkerdeep neural network |
| spellingShingle | Kai He Yan Wang Xuping Xie Dan Shao Prediction of Proteins in Cerebrospinal Fluid and Application to Glioma Biomarker Identification Molecules cerebrospinal fluid glioma biomarker deep neural network |
| title | Prediction of Proteins in Cerebrospinal Fluid and Application to Glioma Biomarker Identification |
| title_full | Prediction of Proteins in Cerebrospinal Fluid and Application to Glioma Biomarker Identification |
| title_fullStr | Prediction of Proteins in Cerebrospinal Fluid and Application to Glioma Biomarker Identification |
| title_full_unstemmed | Prediction of Proteins in Cerebrospinal Fluid and Application to Glioma Biomarker Identification |
| title_short | Prediction of Proteins in Cerebrospinal Fluid and Application to Glioma Biomarker Identification |
| title_sort | prediction of proteins in cerebrospinal fluid and application to glioma biomarker identification |
| topic | cerebrospinal fluid glioma biomarker deep neural network |
| url | https://www.mdpi.com/1420-3049/28/8/3617 |
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