Oral Presentation 12

日時： 2020 年 10 月 30 日 14:45-16:15
チャンネル： ３

口頭発表１２『バイオインフォマティックス』
Bioinformatics
座長： 清水 祐吾（慶應義塾大学）
Yugo Shimizu (Keio University)
O3-12"Development of machine learning based prediction model for hypertensive disordersof pregnancy (HDP)"
Satoshi Mizuno(Tohoku Medical Megabank Organization, Tohoku University)
O3-13"Selecting the genes related to COVID-19 with PCA-based unsupervised feature
extraction"
Kota Fujisawa(Graduate school of Engineering and Science, University of the Ryukyus)

O3-12Development of machine learning based prediction modelfor hypertensive disorders of pregnancy (HDP)
Satoshi Mizuno1
samizuno@med.tohoku.ac.jp

Satoshi Nagaie 1

Shinichi Kuriyama 3kuriyama@med.tohoku.ac.jp

Hiroshi Tanaka 4tanaka@cim.tmd.ac.jp

Junichi Sugawara 6

jsugawara@med.tohoku.ac.jp
1

2

3

4

5

6

Gen Tamiya 2

satoshi.nagaie@gmail.com gtamiya@genetix-h.com

Nobuo Yaegashi 5
nobuo.yaegashi@gmail.com

Soichi Ogishima 1

ogishima@megabank.tohoku.ac.jp

Department of Informatics for Genomic Medicine, Group of Integrated Database Systems,Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
Department of Statistical Genetics and Genomics, Group of Disease Risk Prediction, Tohoku
Medical Megabank Organization, Tohoku University, Miyagi, Japan
Department of Molecular Epidemiology, Group of the Birth and Three-Generation Cohort Study,
Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
Department of Bioclinical Informatics, Group of Integrated Database Systems, Tohoku Medical
Megabank Organization, Tohoku University, Miyagi, Japan
Department of Gynecology and Obstetrics, Tohoku University Graduate School of Medicine,Tohoku University, Miyagi, Japan
Department of Feto-Maternal Medical Science, Group of Community Medical Supports,
Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan

Keywords: Machine learning, hypertensive disorders of pregnancy, Phenotyping
In the resent years, machine learning (ML) is widely applied to clinical tasks including detection oftumors from medical images [1] and predict clinical events from electronic health records（EHRs）[2].
ML is also expected to be applied to early prediction of common diseases such as diabetes and heart
failure from various data including genetic factors and exposures. As major limitations underlying
current effort to early prediction of common diseases from big data, static and informatic issues
including multi-modality, high dimension and variety of data remain to be addressed.
In this study, we developed prediction model of hypertensive disorders of pregnancy(HDP) with 22,256 pregnancy women in the BirThree cohort study [3]. Time-series
exposures, laboratory tests and medical records were used as input data. To address staticand informatic issue, we compared predictive power of several types of ML models andpreprocessing methods in combination. Both training and test labels were identified bypreviously developed precise phenotyping algorism (PPV = 0.94). Evaluation of predictive
powers were performed based on ten-fold cross validation.
The predictive power of developed ML model was up-to 0.95 of F1 score. Among thedeveloped models, interpretable models show high importance for blood pressure aroundthe mean of onset, eating habit and lifestyle.
Our developed ML models enable us not only to conduct risk prediction but also knowledge
acquisition for drug development of HDP.

[1] S Kudo et al, Artificial Intelligence-assisted System Improves Endoscopic Identification ofColorectal Neoplasms, Clin Gastroenterol Hepatol, 18(8):1874-1881.e2, 2020
[2] A Rajkomar et al, Scalable and accurate deep learning with electronic health Records, NPJ
Digit Med, 8;1:18, 2018.
[3] S Kuriyama et al, The Tohoku Medical Megabank Project: Design and Misson, J Epidemiol,
26(9):493-511, 2016.

O3-13
Selecting the genes related to COVID-19 with
PCA-based unsupervised feature extraction
Kota Fujisawa 1

Mamoru Shimo 2

k198422@eve.u-ryukyu.ac.jp

e175238@eve.u-ryukyu.ac.jp

Yoshihiro Taguchi 3Shinya Ikematu 4

tag@granular.com

ikematsu@okinawa-ct.ac.jp1234

Ryota Miyata 2
miyata26@tec.u-ryukyu.ac.jp

Graduate school of engineering and science, University of the Ryukyus, 1 Senbaru, Nishihara,Nakagami, Okinawa 903-0213, Japan
Faculty of Engineering, University of the Ryukyus, 1 Senbaru, Nishihara, Nakagami, Okinawa
903-0213, Japan
Faculty of Science and Engineering, Chuo University, 1-13-27 Kasuga, Bunkyo, Tokyo112-8551,Japan
Department of Bioresources Engineering, National Institute of Technology, Okinawa College,905 Henoko, Nago, Okinawa 905-2192, Japan

Keywords: COVID-19, SARS-CoV-2, machine learning, gene selection
Coronavirus disease 2019 (COVID-19) is raging all over the world. This potentially fatal
infectious disease is caused by severe acute respiratory syndr ome coronavirus 2 (SARS-CoV-2).
However, the mechanism of COVID-19 is not well understood. Therefore, we analyze the gene
expression profiles of COVID-19-infected patients to identify the disease-related genes using an
innovative machine learning method, which allows us to perform gene selection from a datasetwith small samples and many candidates based on a data-driven strategy.
First, we applied Principal-components-analysis-based unsupervised feature extraction (PCAUFE[1]) to the mRNA expression profiles of 17 patients and 17 healthy controls (GSE152418 [4]),identifying 123 genes as critical for COVID-19 progression from 60,683 candidate genes. Second,
we also applied PCAUFE to GSE1739 [2], a dataset of SARS, which was the other bat-origin
coronavirus disease and was caused by SARS-CoV. An integrated analysis of the two datasets
revealed 83 genes uniquely selected from the COVID -19 dataset, such as B2M, EIF4G2, andHLA-DPA1. Moreover, we also found 40 genes commonly selected from both the datasets such asCD74, HLA-DRA, and HLA-DRB1.
Finally, to investigate the biological reliability of these selected genes, we uploaded them anenrichment analysis sever called GeneSetDB [3]. Both the 83 genes unique to the COVID-19
dataset and the 40 genes common to these zoonotic-coronavirus datasets mainly included
immune-related genes. These results suggest that PCAUFE could successfully identify abiologically feasible set of COVID-19-related genes.
[1] Taguchi, Y., Unsupervised Feature Extraction Applied to Bioinformatics: A P CA Based and TD
Based Approach, Springer, 2019.
[2] Reghunathan, R., et al. , Expression profile of immune response genes in patients with Severe
Acute Respiratory Syndrome, BMC Immunol, 6:2, 2005.
[3] https://genesetdb.auckland.ac.nz/haeremai.html[4] https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152418