Predicting COVID-19 Severity Using a Cut-and-Solve Feature Selection Approach

Kenneth Smith; Michael Chan; John Brandenburg; Katarina A. Jones; Shawn R. Campagna; Michael Garvin; Alan R. Templeton; Daniel Jacobson; Carlos Cruchaga; Sharlee Climer

doi:10.1109/BIBM58861.2023.10385322

Predicting COVID-19 Severity Using a Cut-and-Solve Feature Selection Approach

Kenneth Smith, Michael Chan, John Brandenburg, Katarina A. Jones, Shawn R. Campagna, Michael Garvin, Alan R. Templeton, Daniel Jacobson, Carlos Cruchaga, Sharlee Climer

Computational and Predictive Biology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Individuals with coronavirus disease 2019 (COVID-19) infection present in a variety of ways, ranging from asymptomatic or mild cough, to organ failure or death. One of the major challenges for the medical community is the quick and accurate determination of how COVID-19 will progress in an individual. Herein, we introduce a new Cut-and-Solve based feature selection program for identifying predictive feature sets in heterogeneous data. We analyze proteomics data from Washington University to identify models ranging in size from a single feature up to five. Validation of logistic regression models using area under the curve (AUC) were applied for both a holdout data set and an independent data set from Massachusetts General Hospital. A variety of known and novel biomarkers for COVID-19 severity were identified. The best model for predicting severe (ventilation or death) vs. non-severe infection is achieved for CALCOCO2 and STC1, with an average AUC=0.81. Based on the known severity markers, several different proteomic pathways are identified. Enrichment analysis indicates activity associated with inflammatory response, as well as myelination and cardiac function.

Original language	English
Title of host publication	Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
Editors	Xingpeng Jiang, Haiying Wang, Reda Alhajj, Xiaohua Hu, Felix Engel, Mufti Mahmud, Nadia Pisanti, Xuefeng Cui, Hong Song
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3370-3375
Number of pages	6
ISBN (Electronic)	9798350337488
DOIs	https://doi.org/10.1109/BIBM58861.2023.10385322
State	Published - 2023
Event	2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023 - Istanbul, Turkey Duration: Dec 5 2023 → Dec 8 2023

Publication series

Name	Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

Conference

Conference	2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
Country/Territory	Turkey
City	Istanbul
Period	12/5/23 → 12/8/23

Funding

VI. ACKNOWLEDGEMENTS The computation for this work was performed on the high performance computing infrastructure provided by Research Computing Support Services and in part by the National Science Foundation under grant number CNS-1429294 at the University of Missouri, Columbia MO. DOI: https://doi.org/10.32469/10355/69802

Keywords

COVID-19
Feature Selection
Mixed Integer Programming

Access to Document

10.1109/BIBM58861.2023.10385322

Cite this

Smith, K., Chan, M., Brandenburg, J., Jones, K. A., Campagna, S. R., Garvin, M., Templeton, A. R., Jacobson, D., Cruchaga, C., & Climer, S. (2023). Predicting COVID-19 Severity Using a Cut-and-Solve Feature Selection Approach. In X. Jiang, H. Wang, R. Alhajj, X. Hu, F. Engel, M. Mahmud, N. Pisanti, X. Cui, & H. Song (Eds.), Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023 (pp. 3370-3375). (Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM58861.2023.10385322

Smith, Kenneth ; Chan, Michael ; Brandenburg, John et al. / Predicting COVID-19 Severity Using a Cut-and-Solve Feature Selection Approach. Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023. editor / Xingpeng Jiang ; Haiying Wang ; Reda Alhajj ; Xiaohua Hu ; Felix Engel ; Mufti Mahmud ; Nadia Pisanti ; Xuefeng Cui ; Hong Song. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 3370-3375 (Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023).

@inproceedings{e7574235b48447c4b552783c0650bba8,

title = "Predicting COVID-19 Severity Using a Cut-and-Solve Feature Selection Approach",

abstract = "Individuals with coronavirus disease 2019 (COVID-19) infection present in a variety of ways, ranging from asymptomatic or mild cough, to organ failure or death. One of the major challenges for the medical community is the quick and accurate determination of how COVID-19 will progress in an individual. Herein, we introduce a new Cut-and-Solve based feature selection program for identifying predictive feature sets in heterogeneous data. We analyze proteomics data from Washington University to identify models ranging in size from a single feature up to five. Validation of logistic regression models using area under the curve (AUC) were applied for both a holdout data set and an independent data set from Massachusetts General Hospital. A variety of known and novel biomarkers for COVID-19 severity were identified. The best model for predicting severe (ventilation or death) vs. non-severe infection is achieved for CALCOCO2 and STC1, with an average AUC=0.81. Based on the known severity markers, several different proteomic pathways are identified. Enrichment analysis indicates activity associated with inflammatory response, as well as myelination and cardiac function.",

keywords = "COVID-19, Feature Selection, Mixed Integer Programming",

author = "Kenneth Smith and Michael Chan and John Brandenburg and Jones, {Katarina A.} and Campagna, {Shawn R.} and Michael Garvin and Templeton, {Alan R.} and Daniel Jacobson and Carlos Cruchaga and Sharlee Climer",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023 ; Conference date: 05-12-2023 Through 08-12-2023",

year = "2023",

doi = "10.1109/BIBM58861.2023.10385322",

language = "English",

series = "Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3370--3375",

editor = "Xingpeng Jiang and Haiying Wang and Reda Alhajj and Xiaohua Hu and Felix Engel and Mufti Mahmud and Nadia Pisanti and Xuefeng Cui and Hong Song",

booktitle = "Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023",

}

Smith, K, Chan, M, Brandenburg, J, Jones, KA, Campagna, SR, Garvin, M, Templeton, AR, Jacobson, D, Cruchaga, C & Climer, S 2023, Predicting COVID-19 Severity Using a Cut-and-Solve Feature Selection Approach. in X Jiang, H Wang, R Alhajj, X Hu, F Engel, M Mahmud, N Pisanti, X Cui & H Song (eds), Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023. Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023, Institute of Electrical and Electronics Engineers Inc., pp. 3370-3375, 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023, Istanbul, Turkey, 12/5/23. https://doi.org/10.1109/BIBM58861.2023.10385322

Predicting COVID-19 Severity Using a Cut-and-Solve Feature Selection Approach. / Smith, Kenneth; Chan, Michael; Brandenburg, John et al.
Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023. ed. / Xingpeng Jiang; Haiying Wang; Reda Alhajj; Xiaohua Hu; Felix Engel; Mufti Mahmud; Nadia Pisanti; Xuefeng Cui; Hong Song. Institute of Electrical and Electronics Engineers Inc., 2023. p. 3370-3375 (Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Smith, Kenneth

AU - Chan, Michael

AU - Brandenburg, John

AU - Jones, Katarina A.

AU - Campagna, Shawn R.

AU - Garvin, Michael

AU - Templeton, Alan R.

AU - Jacobson, Daniel

AU - Cruchaga, Carlos

AU - Climer, Sharlee

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N2 - Individuals with coronavirus disease 2019 (COVID-19) infection present in a variety of ways, ranging from asymptomatic or mild cough, to organ failure or death. One of the major challenges for the medical community is the quick and accurate determination of how COVID-19 will progress in an individual. Herein, we introduce a new Cut-and-Solve based feature selection program for identifying predictive feature sets in heterogeneous data. We analyze proteomics data from Washington University to identify models ranging in size from a single feature up to five. Validation of logistic regression models using area under the curve (AUC) were applied for both a holdout data set and an independent data set from Massachusetts General Hospital. A variety of known and novel biomarkers for COVID-19 severity were identified. The best model for predicting severe (ventilation or death) vs. non-severe infection is achieved for CALCOCO2 and STC1, with an average AUC=0.81. Based on the known severity markers, several different proteomic pathways are identified. Enrichment analysis indicates activity associated with inflammatory response, as well as myelination and cardiac function.

AB - Individuals with coronavirus disease 2019 (COVID-19) infection present in a variety of ways, ranging from asymptomatic or mild cough, to organ failure or death. One of the major challenges for the medical community is the quick and accurate determination of how COVID-19 will progress in an individual. Herein, we introduce a new Cut-and-Solve based feature selection program for identifying predictive feature sets in heterogeneous data. We analyze proteomics data from Washington University to identify models ranging in size from a single feature up to five. Validation of logistic regression models using area under the curve (AUC) were applied for both a holdout data set and an independent data set from Massachusetts General Hospital. A variety of known and novel biomarkers for COVID-19 severity were identified. The best model for predicting severe (ventilation or death) vs. non-severe infection is achieved for CALCOCO2 and STC1, with an average AUC=0.81. Based on the known severity markers, several different proteomic pathways are identified. Enrichment analysis indicates activity associated with inflammatory response, as well as myelination and cardiac function.

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M3 - Conference contribution

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T3 - Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

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BT - Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

A2 - Jiang, Xingpeng

A2 - Wang, Haiying

A2 - Alhajj, Reda

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A2 - Pisanti, Nadia

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PB - Institute of Electrical and Electronics Engineers Inc.

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Smith K, Chan M, Brandenburg J, Jones KA, Campagna SR, Garvin M et al. Predicting COVID-19 Severity Using a Cut-and-Solve Feature Selection Approach. In Jiang X, Wang H, Alhajj R, Hu X, Engel F, Mahmud M, Pisanti N, Cui X, Song H, editors, Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 3370-3375. (Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023). doi: 10.1109/BIBM58861.2023.10385322

Predicting COVID-19 Severity Using a Cut-and-Solve Feature Selection Approach

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