Discovery of Stomach Adenocarcinoma Biomarkers by Consensus Scoring of Random Sampling and Machine Learning Modeling

Ji Chen; Yang Hao; Tianjun Wang; Daiyun Huang; Xin Liu

doi:10.1109/ICBCB55259.2022.9802469

Discovery of Stomach Adenocarcinoma Biomarkers by Consensus Scoring of Random Sampling and Machine Learning Modeling

Ji Chen
, Yang Hao
, Tianjun Wang
, Daiyun Huang
, Xin Liu

Academy of Pharmacy

Xi'an Jiaotong-Liverpool University

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

2 Citations (Scopus)

Abstract

Stomach adenocarcinoma (STAD) is a subtype of gastric cancer with high incidence and mortality. Lack of early detection results in the poor prognosis of this cancer, leading to low survival rate of patients. In this study, machine learning methods, specifically support vector machine (SVM) based recursive feature elimination (SVM-RFE), were applied to discover the potential biomarkers of STAD with the data form the Cancer Genome Atlas (TCGA). After the optimal parameter set was determined, random sampling was conducted to minimize the limitation caused by small sample size (64 paired tumor and adjacent non-tumor samples). As a result, five genes (COL10A1, CST1, ESM1, HOXC11 and HOXC9) were identified to be essential to the predictive model built by SVM-RFE. In addition, other three genes GAD1, HOXA11 and PRKCG are of less importance but still could be potential biomarkers of STAD.

Original language	English
Title of host publication	2022 10th International Conference on Bioinformatics and Computational Biology, ICBCB 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	112-115
Number of pages	4
ISBN (Electronic)	9781665401081
DOIs	https://doi.org/10.1109/ICBCB55259.2022.9802469
Publication status	Published - 2022
Event	10th International Conference on Bioinformatics and Computational Biology, ICBCB 2022 - Virtual, Hangzhou, China Duration: 13 May 2022 → 15 May 2022

Publication series

Name	2022 10th International Conference on Bioinformatics and Computational Biology, ICBCB 2022

Conference

Conference	10th International Conference on Bioinformatics and Computational Biology, ICBCB 2022
Country/Territory	China
City	Virtual, Hangzhou
Period	13/05/22 → 15/05/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

RFE
SVM
biomarker
gastric cancer
machine learning
recursive feature elimination
stomach adenocarcinoma
support vector machine

Access to Document

10.1109/ICBCB55259.2022.9802469

Cite this

Chen, J., Hao, Y., Wang, T., Huang, D., & Liu, X. (2022). Discovery of Stomach Adenocarcinoma Biomarkers by Consensus Scoring of Random Sampling and Machine Learning Modeling. In 2022 10th International Conference on Bioinformatics and Computational Biology, ICBCB 2022 (pp. 112-115). (2022 10th International Conference on Bioinformatics and Computational Biology, ICBCB 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICBCB55259.2022.9802469

Chen, Ji ; Hao, Yang ; Wang, Tianjun et al. / Discovery of Stomach Adenocarcinoma Biomarkers by Consensus Scoring of Random Sampling and Machine Learning Modeling. 2022 10th International Conference on Bioinformatics and Computational Biology, ICBCB 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 112-115 (2022 10th International Conference on Bioinformatics and Computational Biology, ICBCB 2022).

@inproceedings{3705f9613583481f940ea50d290759a7,

title = "Discovery of Stomach Adenocarcinoma Biomarkers by Consensus Scoring of Random Sampling and Machine Learning Modeling",

abstract = "Stomach adenocarcinoma (STAD) is a subtype of gastric cancer with high incidence and mortality. Lack of early detection results in the poor prognosis of this cancer, leading to low survival rate of patients. In this study, machine learning methods, specifically support vector machine (SVM) based recursive feature elimination (SVM-RFE), were applied to discover the potential biomarkers of STAD with the data form the Cancer Genome Atlas (TCGA). After the optimal parameter set was determined, random sampling was conducted to minimize the limitation caused by small sample size (64 paired tumor and adjacent non-tumor samples). As a result, five genes (COL10A1, CST1, ESM1, HOXC11 and HOXC9) were identified to be essential to the predictive model built by SVM-RFE. In addition, other three genes GAD1, HOXA11 and PRKCG are of less importance but still could be potential biomarkers of STAD.",

keywords = "RFE, SVM, biomarker, gastric cancer, machine learning, recursive feature elimination, stomach adenocarcinoma, support vector machine",

author = "Ji Chen and Yang Hao and Tianjun Wang and Daiyun Huang and Xin Liu",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 10th International Conference on Bioinformatics and Computational Biology, ICBCB 2022 ; Conference date: 13-05-2022 Through 15-05-2022",

year = "2022",

doi = "10.1109/ICBCB55259.2022.9802469",

language = "English",

series = "2022 10th International Conference on Bioinformatics and Computational Biology, ICBCB 2022",

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

pages = "112--115",

booktitle = "2022 10th International Conference on Bioinformatics and Computational Biology, ICBCB 2022",

}

Chen, J, Hao, Y, Wang, T, Huang, D & Liu, X 2022, Discovery of Stomach Adenocarcinoma Biomarkers by Consensus Scoring of Random Sampling and Machine Learning Modeling. in 2022 10th International Conference on Bioinformatics and Computational Biology, ICBCB 2022. 2022 10th International Conference on Bioinformatics and Computational Biology, ICBCB 2022, Institute of Electrical and Electronics Engineers Inc., pp. 112-115, 10th International Conference on Bioinformatics and Computational Biology, ICBCB 2022, Virtual, Hangzhou, China, 13/05/22. https://doi.org/10.1109/ICBCB55259.2022.9802469

Discovery of Stomach Adenocarcinoma Biomarkers by Consensus Scoring of Random Sampling and Machine Learning Modeling. / Chen, Ji; Hao, Yang; Wang, Tianjun et al.
2022 10th International Conference on Bioinformatics and Computational Biology, ICBCB 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 112-115 (2022 10th International Conference on Bioinformatics and Computational Biology, ICBCB 2022).

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

TY - GEN

T1 - Discovery of Stomach Adenocarcinoma Biomarkers by Consensus Scoring of Random Sampling and Machine Learning Modeling

AU - Chen, Ji

AU - Hao, Yang

AU - Wang, Tianjun

AU - Huang, Daiyun

AU - Liu, Xin

PY - 2022

Y1 - 2022

N2 - Stomach adenocarcinoma (STAD) is a subtype of gastric cancer with high incidence and mortality. Lack of early detection results in the poor prognosis of this cancer, leading to low survival rate of patients. In this study, machine learning methods, specifically support vector machine (SVM) based recursive feature elimination (SVM-RFE), were applied to discover the potential biomarkers of STAD with the data form the Cancer Genome Atlas (TCGA). After the optimal parameter set was determined, random sampling was conducted to minimize the limitation caused by small sample size (64 paired tumor and adjacent non-tumor samples). As a result, five genes (COL10A1, CST1, ESM1, HOXC11 and HOXC9) were identified to be essential to the predictive model built by SVM-RFE. In addition, other three genes GAD1, HOXA11 and PRKCG are of less importance but still could be potential biomarkers of STAD.

AB - Stomach adenocarcinoma (STAD) is a subtype of gastric cancer with high incidence and mortality. Lack of early detection results in the poor prognosis of this cancer, leading to low survival rate of patients. In this study, machine learning methods, specifically support vector machine (SVM) based recursive feature elimination (SVM-RFE), were applied to discover the potential biomarkers of STAD with the data form the Cancer Genome Atlas (TCGA). After the optimal parameter set was determined, random sampling was conducted to minimize the limitation caused by small sample size (64 paired tumor and adjacent non-tumor samples). As a result, five genes (COL10A1, CST1, ESM1, HOXC11 and HOXC9) were identified to be essential to the predictive model built by SVM-RFE. In addition, other three genes GAD1, HOXA11 and PRKCG are of less importance but still could be potential biomarkers of STAD.

KW - RFE

KW - SVM

KW - biomarker

KW - gastric cancer

KW - machine learning

KW - recursive feature elimination

KW - stomach adenocarcinoma

KW - support vector machine

UR - https://www.scopus.com/pages/publications/85134307780

U2 - 10.1109/ICBCB55259.2022.9802469

DO - 10.1109/ICBCB55259.2022.9802469

M3 - Conference Proceeding

AN - SCOPUS:85134307780

T3 - 2022 10th International Conference on Bioinformatics and Computational Biology, ICBCB 2022

SP - 112

EP - 115

BT - 2022 10th International Conference on Bioinformatics and Computational Biology, ICBCB 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 10th International Conference on Bioinformatics and Computational Biology, ICBCB 2022

Y2 - 13 May 2022 through 15 May 2022

ER -

Chen J, Hao Y, Wang T, Huang D , Liu X. Discovery of Stomach Adenocarcinoma Biomarkers by Consensus Scoring of Random Sampling and Machine Learning Modeling. In 2022 10th International Conference on Bioinformatics and Computational Biology, ICBCB 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 112-115. (2022 10th International Conference on Bioinformatics and Computational Biology, ICBCB 2022). doi: 10.1109/ICBCB55259.2022.9802469

Discovery of Stomach Adenocarcinoma Biomarkers by Consensus Scoring of Random Sampling and Machine Learning Modeling

Abstract

Publication series

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UN SDGs

Keywords

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