Using Data-Driven Algorithms with Large-Scale Plasma Proteomic Data to Discover Novel Biomarkers for Diagnosing Depression
Given recent technological advances in proteomics, it is now possible to quantify plasma proteomes in large cohorts of patients to screen for biomarkers and to guide the early diagnosis and treatment of depression. Here we used CatBoost machine learning to model and discover biomarkers of depression...
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| Vydáno v: | Journal of proteome research Ročník 23; číslo 9; s. 4043 |
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| Hlavní autoři: | , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
United States
06.09.2024
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| ISSN: | 1535-3907, 1535-3907 |
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| Abstract | Given recent technological advances in proteomics, it is now possible to quantify plasma proteomes in large cohorts of patients to screen for biomarkers and to guide the early diagnosis and treatment of depression. Here we used CatBoost machine learning to model and discover biomarkers of depression in UK Biobank data sets (depression
= 4,479, healthy control
= 19,821). CatBoost was employed for model construction, with Shapley Additive Explanations (SHAP) being utilized to interpret the resulting model. Model performance was corroborated through 5-fold cross-validation, and its diagnostic efficacy was evaluated based on the area under the receiver operating characteristic (AUC) curve. A total of 45 depression-related proteins were screened based on the top 20 important features output by the CatBoost model in six data sets. Of the nine diagnostic models for depression, the performance of the traditional risk factor model was improved after the addition of proteomic data, with the best model having an average AUC of 0.764 in the test sets. KEGG pathway analysis of 45 screened proteins showed that the most significant pathway involved was the cytokine-cytokine receptor interaction. It is feasible to explore diagnostic biomarkers of depression using data-driven machine learning methods and large-scale data sets, although the results require validation. |
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| AbstractList | Given recent technological advances in proteomics, it is now possible to quantify plasma proteomes in large cohorts of patients to screen for biomarkers and to guide the early diagnosis and treatment of depression. Here we used CatBoost machine learning to model and discover biomarkers of depression in UK Biobank data sets (depression n = 4,479, healthy control n = 19,821). CatBoost was employed for model construction, with Shapley Additive Explanations (SHAP) being utilized to interpret the resulting model. Model performance was corroborated through 5-fold cross-validation, and its diagnostic efficacy was evaluated based on the area under the receiver operating characteristic (AUC) curve. A total of 45 depression-related proteins were screened based on the top 20 important features output by the CatBoost model in six data sets. Of the nine diagnostic models for depression, the performance of the traditional risk factor model was improved after the addition of proteomic data, with the best model having an average AUC of 0.764 in the test sets. KEGG pathway analysis of 45 screened proteins showed that the most significant pathway involved was the cytokine-cytokine receptor interaction. It is feasible to explore diagnostic biomarkers of depression using data-driven machine learning methods and large-scale data sets, although the results require validation.Given recent technological advances in proteomics, it is now possible to quantify plasma proteomes in large cohorts of patients to screen for biomarkers and to guide the early diagnosis and treatment of depression. Here we used CatBoost machine learning to model and discover biomarkers of depression in UK Biobank data sets (depression n = 4,479, healthy control n = 19,821). CatBoost was employed for model construction, with Shapley Additive Explanations (SHAP) being utilized to interpret the resulting model. Model performance was corroborated through 5-fold cross-validation, and its diagnostic efficacy was evaluated based on the area under the receiver operating characteristic (AUC) curve. A total of 45 depression-related proteins were screened based on the top 20 important features output by the CatBoost model in six data sets. Of the nine diagnostic models for depression, the performance of the traditional risk factor model was improved after the addition of proteomic data, with the best model having an average AUC of 0.764 in the test sets. KEGG pathway analysis of 45 screened proteins showed that the most significant pathway involved was the cytokine-cytokine receptor interaction. It is feasible to explore diagnostic biomarkers of depression using data-driven machine learning methods and large-scale data sets, although the results require validation. Given recent technological advances in proteomics, it is now possible to quantify plasma proteomes in large cohorts of patients to screen for biomarkers and to guide the early diagnosis and treatment of depression. Here we used CatBoost machine learning to model and discover biomarkers of depression in UK Biobank data sets (depression = 4,479, healthy control = 19,821). CatBoost was employed for model construction, with Shapley Additive Explanations (SHAP) being utilized to interpret the resulting model. Model performance was corroborated through 5-fold cross-validation, and its diagnostic efficacy was evaluated based on the area under the receiver operating characteristic (AUC) curve. A total of 45 depression-related proteins were screened based on the top 20 important features output by the CatBoost model in six data sets. Of the nine diagnostic models for depression, the performance of the traditional risk factor model was improved after the addition of proteomic data, with the best model having an average AUC of 0.764 in the test sets. KEGG pathway analysis of 45 screened proteins showed that the most significant pathway involved was the cytokine-cytokine receptor interaction. It is feasible to explore diagnostic biomarkers of depression using data-driven machine learning methods and large-scale data sets, although the results require validation. |
| Author | Kang, Lijun Ma, Simeng Liu, Zhongchun Li, Ruiling Gong, Qian Deng, Zipeng Yang, Jun Yao, Lihua Xiang, Dan Lv, Honggang Wang, Beibei |
| Author_xml | – sequence: 1 givenname: Simeng orcidid: 0000-0002-1775-5202 surname: Ma fullname: Ma, Simeng organization: Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China – sequence: 2 givenname: Ruiling surname: Li fullname: Li, Ruiling organization: Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China – sequence: 3 givenname: Qian surname: Gong fullname: Gong, Qian organization: Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China – sequence: 4 givenname: Honggang surname: Lv fullname: Lv, Honggang organization: Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China – sequence: 5 givenname: Zipeng surname: Deng fullname: Deng, Zipeng organization: Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China – sequence: 6 givenname: Beibei surname: Wang fullname: Wang, Beibei organization: Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China – sequence: 7 givenname: Lihua surname: Yao fullname: Yao, Lihua organization: Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China – sequence: 8 givenname: Lijun surname: Kang fullname: Kang, Lijun organization: Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China – sequence: 9 givenname: Dan surname: Xiang fullname: Xiang, Dan organization: Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China – sequence: 10 givenname: Jun surname: Yang fullname: Yang, Jun organization: School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China – sequence: 11 givenname: Zhongchun surname: Liu fullname: Liu, Zhongchun organization: Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan 430071, China |
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| Keywords | Biomarkers Depression CatBoost Proteomic |
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| SubjectTerms | Algorithms Area Under Curve Biomarkers - blood Blood Proteins - analysis Blood Proteins - metabolism Depression - blood Depression - diagnosis Female Humans Machine Learning Male Proteome - analysis Proteome - metabolism Proteomics - methods ROC Curve |
| Title | Using Data-Driven Algorithms with Large-Scale Plasma Proteomic Data to Discover Novel Biomarkers for Diagnosing Depression |
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