HEEWON PARK (PARK HEEWON) - マイポータル

基本情報

所属: 東京医科歯科大学

J-GLOBAL ID: 201801011658112929
researchmap会員ID: 7000021823

経歴

2018年9月 - 現在

Laboratory of DNA Information Analysis, Human Genome Center, Institute of Medical Science, University of Tokyo, Visiting researcher （客員研究員）
2017年5月 - 現在

High-dimensional Statistical Modeling Unit, RIKEN AIP, Visiting researcher （客員研究員）
2015年4月 - 現在

Faculty of Global Science Studies, Yamaguchi University, Assistant professor
2013年4月 - 2015年3月

Laboratory of DNA Information Analysis, Human Genome Center, Institute of Medical Science, University of Tokyo, Post-Doctoral Fellow

学歴

2010年4月 - 2013年3月

Ph.D, Department of Mathematics, Chuo University, Tokyo, Japan.

論文

Robust sample-specific stability selection with effective error control.

H. Park, M. Yamada, S. Imoto, S. Miyano

Journal of Computational Biology To appear 2018年11月査読有り
Sparse common component analysis for multiple high-dimensional datasets via non-centered principal component analysis.

Park H, Konishi S

Statistical papers To appear 2018年9月査読有り
Adaptive NetworkProfiler for Identifying Cancer Characteristic-Specific Gene Regulatory Networks

Heewon Park, Teppei Shimamura, Seiya Imoto, Satoru Miyano

Journal of Computational Biology 25(2) 130-145 2018年2月1日査読有り
A Novel Adaptive Penalized Logistic Regression for Uncovering Biomarker Associated with Anti-Cancer Drug Sensitivity

Heewon Park, Yuichi Shiraishi, Seiya Imoto, Satoru Miyano

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 14(4) 771-782 2017年7月査読有り
Outlier-resistant high-dimensional regression modelling based on distribution-free outlier detection and tuning parameter selection

Heewon Park

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 87(9) 1799-1812 2017年6月査読有り
Principal component selection via adaptive regularization method and generalized information criterion

Heewon Park, Sadanori Konishi

STATISTICAL PAPERS 58(1) 147-160 2017年3月査読有り
Interaction-Based Feature Selection for Uncovering Cancer Driver Genes Through Copy Number-Driven Expression Level

Heewon Park, Atsushi Niida, Seiya Imoto, Satoru Miyano

JOURNAL OF COMPUTATIONAL BIOLOGY 24(2) 138-152 2017年2月査読有り
ビッグデータと生命科学 9.「京」を使った大規模データ解析によるがんのシステム異常の網羅的解析

宮野悟, 宮野悟, 伊東聰, PARK Heewon, 白石友一, 島村徹平, 玉田嘉紀, 井元清哉

実験医学 34(5) 733-738 2016年3月15日
Robust logistic regression modelling via the elastic net-type regularization and tuning parameter selection

Heewon Park, Sadanori Konishi

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 86(7) 1450-1461 2016年査読有り
Robust Coordinate Descent Algorithm Robust Solution Path for High-dimensional Sparse Regression Modeling

H. Park, S. Konishi

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 45(1) 115-129 2016年査読有り
Robust regularized nonlinear regression modeling via -type approach.

Park H, Konishi S

Bulletin Informatics and Cybernetics 2016年査読有り
Recursive Random Lasso (RRLasso) for Identifying Anti-Cancer Drug Targets

Heewon Park, Seiya Imoto, Satoru Miyano

PLOS ONE 10(11) e0141869 2015年11月査読有り
Sparse Overlapping Group Lasso for Integrative Multi-Omics Analysis

Heewon Park, Atushi Niida, Satoru Miyano, Seiya Imoto

JOURNAL OF COMPUTATIONAL BIOLOGY 22(2) 73-84 2015年2月査読有り
Robust Prediction of Anti-Cancer Drug Sensitivity and Sensitivity-Specific Biomarker

Heewon Park, Teppei Shimamura, Satoru Miyano, Seiya Imoto

PLOS ONE 9(10) e108990 2014年10月査読有り
Robust sparse regression and tuning parameter selection via the efficient bootstrap information criteria

Heewon Park, Fumitake Sakaori, Sadanori Konishi

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 84(7) 1596-1607 2014年7月査読有り
Forecasting symbolic candle chart-valued time series

Park H, Sakaori F

Communications for Statistical Applications and Methods 21 471-486 2014年査読有り
Lag weighted lasso for time series model

Heewon Park, Fumitake Sakaori

Computational Statistics 28(2) 493-504 2013年査読有り
Novel resampling methods for tuning parameter selection in robust sparse regression modeling

Park H

Bulletin Informatics and Cybernetics 44 49-64 2012年査読有り
Selection of tuning parameters in robust sparse regression modeling

Park H, Sakaori F, Konishi S

Proceedings of COMPSTAT2012 713-723 2012年査読有り
Modeling symbolic candle chart time series

PARK Heewon, SAKAORI Fumitake

日本計算機統計学会シンポジウム論文集 25th 21-24 2011年

もっとみる

講演・口頭発表等

A sparse tensor subspace method for identifying biological modulators based on multilayer gene network analysis

Park H, Yamaguchi R, Imoto S, Miyano S

COMPSTAT2018 2018年
Cancer characteristic-specific analysis via L1-type regularized regression modeling.

Park H, Imoto S, Miyano S

科研費シンポジウム: 生命・自然科学における複雑現象解明のための統計的アプローチ 2018年招待有り
Sparse overlapping group lasso for gene selection by incorporating biological knowledge

Park H, Niida A, Imoto S, Miyano S

International Conference on Mathematics & Computer Science 2017 2017年
Interaction based random elastic net for cancer driver gene selection

Park H, Niida A, Imoto S, Miyano S

61th World Statistics Congress (ISI2017) 2017年
Robust sparse logistic regression modeling and tuning parameter selection

Park H, Konishi S

2016 CSA & NCCU Joint Statistical Meetings (In Celebration of the 50th Anniversary of Department of Statistics) 2017年招待有り
Constructing cancer characteristic-specific gene regulatory networks via L1-type regularized regression modeling

Park H, Shimamura T, Imoto S, Miyano S

Conference of the International Federation of Classification Societies (IFCS2017) 2017年招待有り
Sparse modeling for sample-specific analysis

Park H, Imoto S, Miyano S

Japanese Joint Statistical Meeting 2017年招待有り
Interaction based feature selection for driver gene selection via copy number driven expression levels

Park H, Niida A, Imoto S, Miyano S

9th World Congress in Probability and Statistics 2016年
Interaction based gene selection based on copy number driven expression levels.

Park H

Symposium of Kyushu University: 高精度情報抽出のための統計理論・方法論とその応用 2016年招待有り
Statistical methods for identifying patient-specific gene networks determining sensitivity for anti-cancer drug

Park H, Shimamura T, Imoto S, Miyano S

FY2014 Strategic Programs for Innovative Research Field 1 workshop 2015年
Recursive bootstrap L1-type regularized regression modeling based on parametric statistical test

Park H, Imoto S, Miyano S

60th World Statistics Congress (ISI2015) 2015年
Identifying patient-specific biomarker and predicting anti-cancer drug sensitivity via robust statistical methodology

Park H, Shimamura T, Imoto S, Miyano S

JSM 2014 2014年
Adaptive penalized logistic regression for uncovering biomarker

Park H, Shiraishi Y, Imoto S, Miyano S

COMPSTAT2014 2014年
Robust coordinate descending algorithm for high dimensional regression modeling

Park H, Konishi S

Joint Meeting of the IASC Satellite Conference for the 59th ISI WSC 2013年
Predicting anti-cancer drug sensitivity and uncovering cancer biomarker via novel statistical modeling strategies

Park H, Niida A, Shimamura T, Imoto S, Miyano S

FY2013 Strategic Programs for Innovative Research Field 1 workshop 2013年
Robust sparse regression using the efficient bootstrap resampling

Park H, Sakaori F, Konishi S

8th World Congress in Probability and Statistics 2012年
Information criteria for the robust lasso type approaches via local quadratic approximations

Park H, Sakaori F, Konishi S

2012年
Time series model for "candlestick chart"-valued data

Park H, Sakaori F

複雑データの解析に関する研究集会 2011年
Lag weighed lasso

Park H, Sakaori F

5th Japanese statistics society conference 2011年
Penalization method for sparse time series model

Park H, Sakaori F

58th World Statistics Congress (ISI2011) 2011年

もっとみる