Takanori Hasegawa, Ph.D

Associate Professor at Tokyo Medical and Dental University

Visiting Researcher at The University of Tokyo

Co-founder and Collaborative Researcher at miup Inc.

Business and Technology Advisor at CAREER CO.(CareLab), LTD. and Scala Group.

ABOUT

He obtained his Ph.D. on statistical time series analysis and bioinformatics from the Graduate School of Informatics, Kyoto University. During his doctoral studies, he also co-founded a start-up called "CRUNCHERS Inc.," to bring Information Science together with Art. At CRUNCHERS Inc., he developed a novel evaluation system centered on natural language processing technology and a box-office revenue forecasting system for movies (CRUNCH CINEMA). The start-up transferred its business in January 2015.

After his Ph.D., he became an Assistant Professor at Tohoku Medical Megabank Organization, Tohoku University, where he focused on genome wide association study (GWAS) especially for rapid analysis of rare mutations. He also became a technical advisor at Medley inc., where he developed an AI-based (using Bayesian statistical model) medical disease inference system and an influenza-infection prediction system based on time-series data.

He moved to an Assistant Professor at the University of Tokyo in 2015, where he had four key focus areas. First, he was involved in the development of a novel statistical methodology for the inference of the internal states of time series data and the prediction of their future states. Second, he focused on cancer-immunogenomics and the development of their analysis tools in the International Cancer Genome Consortium (ICGC). Third, he handled the stratification analysis of GWAS using cohort data, and lastly, he was involved in the predictive analysis of time-series health check-up data integrating blood test values, social status, lifestyle habits, genomic data, and so on.

He was appointed to his current position in 2020, where he continues to his previous four areas and started to promote collaborative researches and establish analysis environments/education in Tokyo Medical and Dental University. He also has experience serving on committees and evaluation boards at various ministries and agencies. He has extensive knowledge of labor laws and dispatch law operations at medical sites.

EDUCATION

Doctor of Informatics
Department of Intelligence Science and Technology
Graduate School of Informatics, Kyoto University
(Bioinformatics Center, Institute for Chemical Research, Kyoto University)

Doctoral Program
Thesis Title: Reconstructing Biological Systems Incorporating Multi-Source Biological Data via Data Assimilation Techniques

From Apr. 2012 to Jan. 2015 (Completion in shortened years)

Master of Information Science and Technology
Department of Computer Science
Graduate School of Information Science, The University of Tokyo
(Human Genome Center, Institute of Medical Science, The University of Tokyo)

Master's Program
Thesis Title: Intracellular Systems Analysis by Assimilating Large Scale Biological Data and Pathway Simulation

From Apr. 2010 to Mar. 2012

Bachelor of Engineering
Facalty of Science and Engineering, Waseda University

Baccalaureate Degree Program
Thesis Title: 遺伝子ネットワーク推定とデータ同化による細胞内ネットワークシミュレーションモデルの構築

From Apr. 2006 to Mar. 2010

CURRENT WORK

Associate Professor
Department of Integrated Analytics, M&D Data Science Center, Tokyo Medical Dental University

My research topics are analyzing human genome, transcriptome, epigenome, metagenome and clinical data for personalized and preventive medicine through modeling, prediction and inference of disease and health systems. For example, we have been undertaking immunological cancer genome and metagenome analysis based on statistical and machine learning methods.

Especially, in the field of statistical science, my speciality is computational statistics and stochastic simulation, such as data assimilation and have published many research papers.

In addition, I'm promoting and establishing collaborative researches with medical and informatics units in TMDU and education for medical informatics.

September 2021 - Current

Visiting Researcher
Human Genome Center, The Institute of Medical Science, The University of Tokyo

The research topics are the same as those described in TMDU.

October 2021 - Current

Co-founder / Collaborative Researcher
miup

miup Inc. is trying to establish "Integrated Healthcare Eco-System" using IoT technology and clinical A.I. system starting from developing countries to developed countries. Firstly, we have launched a home delivery health-care service in Bangladesh.

My role is to design business strategy, make financial indication and capital policy, raise funds, and manage our team.

Our system has a highly complicated statistical structure to enhance the quality of the inference, estimate the confidence level of the conclusions, and predict the future risks through Bayesian statistics and machine learning.

September 2015 - Current

Business and Technology Advisor
CAREER CO., LTD

Detail is Confidential: Development of new business, and Operation and Production optimization.

July 2019 - Current

Business and Technology Advisor
Scala Group

Detail is Confidential: Development of new business and Evaluation of start-up companies.

Aug. 2019 - Current

PAST WORK

Junior Associate Professor
Department of Integrated Analytics, M&D Data Science Center, Tokyo Medical Dental University

April 2020 - Current

Assistant Professor
Health Intelligence Center, The Institute of Medical Science, The University of Tokyo

October 2015 - March 2020

Technology Advisor
Panasonic Inc.

Detail is Confidential: Production and experiment optimization of an image processing devision based on Bayesian framework.

April 2017 - March 2019

Technology Advisor
Nadia inc.

Detail is Confidential: Education for A.I. development especially for image processing.

March 2016 - February 2017

Technology Advisor
Medley, Inc.

We developed an automated consulting system termed Mogul for medical treatment. This system is a type of artificial intelligence and based on a simple Bayesian inference procedure (Its concept is similar to the one in miup Inc. but this version is a little bit older). Also, a pandemic prediction system for the degree of future influenza epidemic has been released in the website.

February 2015 - September 2017

Assistant Professor
Tohoku Medical Megaban Organization, Tohoku University

The main topic was genome wide association study and to develop novel strategies for analyzing rare variants association test. Our developed method is highly efficient and available to WGS data within a reasonable time. In addition, we proposed a novel statistical method termed kernel Bayes' approximate Bayesian computation filtering for estimation of parameter values in stochastic simulation models.

February 2015 - September 2015

Co-founder / CTO (Technology Advisor, Feb. 2015-)
CRUNCHERS Inc.

We developed automated evaluation systems for predicting the profit of books and cinema. The former system was integrated to Medley, Inc. as CRUNCH MAGAZINE. For the latter system, I still have advised the statistical perspective of applying the developed simulation system (CRUNCH CINEMA) to the collaborative companies. In addition, since we're collaborating with major studios and major entertainment companies in JAPAN and USA, we have extended and developed CRUNCH CINEMA for the application of general purpose.

July 2013 - September 2015

Research Fellow
Japan Society for The Promotion of Science

The research topic was Reconstructing Biological Systems Incorporating Multi-Source Biological Data via Data Assimilation Techniques.

April 2012 - January 2015

PUBLICATIONS

Journal Paper (Corresponding to reseach map)
[Poster] T. Hasegawa, R. Yamaguchi, M. Nagasaki, S. Imoto and S. Miyano. Poster: Comprehensive pharmacogenomic pathway screening by data assimilation. Proceeding of IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), pp246-246, 2011.
[1,F1] T. Hasegawa, R. Yamaguchi, M. Nagasaki, S. Imoto and S. Miyano. Comprehensive pharmacogenomic pathway screening by data assimilation. Proceeding of 7th International Symposium on Bioinformatics Research and Applications, volume 6674 of Lecture Notes in Computer Science, pp.160-171. Springer Berlin Heidelberg, 2011.
[2,F2] T. Hasegawa, R. Yamaguchi, M. Nagasaki, S. Miyano and S. Imoto. Inference of gene regulatory networks incorporating multi-Source biological knowledge via a state space model with L1 regularization. PLOS ONE, vol.9(8), e105942, 2014.
[3,F3] T. Hasegawa, M. Nagasaki, R. Yamaguchi, S. Imoto and S. Miyano. An efficient method of exploring simulation models by assimilating literature and biological observational data, BioSystems, vol.121, pp.54-66, 2014.
[4,F4] T. Hasegawa, T. Mori, R. Yamaguchi, S. Imoto, S. Miyano and T. Akutsu. An efficient data assimilation schema for restoration and extension of gene regulatory networks using time-course observation data. Journal of Computational Biology, vol.21(11), pp.785-798, 2014.
[5 (invited to the jounral paper below)] E. Ayada, T. Hasegawa, A. Niida, S. Miyano and S. Imoto. Binary contingency table Method for analyzing gene mutation in cancer genome. Lecture Notes in Computer Science (Proceeding of 11th The International Symposium on Bioinformatics Research and Applications), vol.9096, pp.12-23, 201.
[6] K. Kojima, Y. Kawai, N. Nariai, T. Mimori, T. Hasegawa and M. Nagasaki. Short tandem repeat number estimation from paired-end sequence reads by considering unobserved genealogy of multiple individuals. Lecture Notes in Computer Science (11th The International Symposium on Bioinformatics Research and Applications), vol.9096, pp.422-423, 2015.
[7,F5] T. Hasegawa, T. Mori, R. Yamaguchi, T. Shimamura, S. Miyano, S. Imoto and T. Akutsu. Genomic data assimilation using a higher moment filtering technique for restoration of gene regulatory networks. BMC Systems Biology, vol.9(14), pp.1-14, 2015.
[8,F6] T. Hasegawa, A. Niida, T. Mori, T. Shimamura, R. Yamaguchi, S. Miyano, T. Akutsu and S. Imoto. A likelihood-free filtering method via approximate Bayesian computation in evaluating biological simulation models. Computational Statistics and Data Analysis, vol.94, pp.63-74, 2016.
[5 (Extended one above)] E. Ayada, T. Hasegawa, A. Niida, S. Miyano and S. Imoto. Binary contingency table Method for analyzing gene mutation in cancer genome. International Journal of Bioinformatics Research and Applications (11th The International Symposium on Bioinformatics Research and Applications), vol.12(3), pp.211-226, 2016.
[9,F7] T. Hasegawa, S. Hayashi, E. Shimizu, S. Mizuno, R. Yamaguchi, S. Miyano, H. Nakagawa and S. Imoto. An in silico automated pipeline to identify tumor specic neoantigens from whole genome and exome sequencing data. Proceeding of 12th International Symposium on Bioinformatics Research and Applications, 2016.
[10,F8] J. Kurashige, T. Hasegawa (Equal First), A. Niida, K. Sugimachi, N. Deng, K. Mima, R. Uchi, G. Sawada, Y. Takahashi, H. Eguchi, M. Inomata, S. Kitano, T. Fukagawa, M. Sasako, H. Sasaki, S. Sasaki, M. Mori, K. Yanagihara, H. Baba, S. Miyano, P. Tan and K Mimori. Integrated molecular profiling of human gastric cancer identifies DDR2 as a potential regulator of peritoneal dissemination. Scientific Reports, vol.6, p.22371, 2016.
[11] S. Koshiba, Y. Yamaguchi, K. Kojima, T. Hasegawa, M. Shirota, T. Saito, D. Saigusa, I. Danjoh, F. Katsuoka, S. Ogishima, Y. Kawai, Y. Yamaguchi-Kabata, M. Sakurai, S. Hirano, J. Nakata, H. Motohashi, A. Hozawa, S. Kuriyama, N. Minegishi, M. Nagasaki, T. Takai-Igarashi, N. Fuse, H. Kiyomoto, J. Sugawara, Y. Suzuki, S. Kure, N. Yaegashi, O. Tanabe, K. Kinoshita, J. Yasuda and M. Yamamoto. The structural origin of metabolic quantitative diversity. Scientific Reports, vol.6, pp.31463, 2016.
[12] K. Kojima, Y. Kawai, N. Nariai, T. Mimori, T. Hasegawa and M. Nagasaki. Short tandem repeat number estimation from paired-end reads for multiple individuals by considering coalescent tree. BMC Genomics, vol.17(Suppl. 5), pp.465-476, 2016.
[13,F9] T. Hasegawa, K. Kojima, Y. Kawai, K. Misawa, T. Mimori and M. Nagasaki. AP-SKAT: highly-efficient genome-wide rare variant association test. BMC Genomics, vol.17(1), pp.1-8, 2016.
[14] T. Morita, A. Rahman, T. Hasegawa, A. Ozaki and T. Tanimoto. The potential possibility of symptom checker. International Journal of Health Policy and Management, vol.6(10), pp.615–616, 2017.
[15,F10] T. Hasegawa, K. Kojima, Y. Kawai and M. Nagasaki. Time-series filtering for replicated observations via a kernel approximate Bayesian computation. IEEE Transactions on Signal Processing, vol.66(23), pp.6148-6161, 2018.
[16] M. Fujita, S. Imoto, R Yamaguchi, T. Hasegawa, S. Hayashi, S. Miyano, H. Yamaue, K. Chayama and H. Nakagawa. Immuno-genomic subtype of liver cancer correlates with mechanisms of immune suppression and prognosis. HUMAN GENOMICS, vol12, 2018.
[17] T. Saito, A. Niida, R. Uchi, H. Hirata, H. Komatsu, S. Sakimura, S. Hayashi, S. Nambara, Y. Kuroda, S. Ito, H. Eguchi, T. Masuda, K. Sugimachi, T. Tobo, H. Nishida, T. Daa, K. Chiba, Y. Shiraishi, T. Yoshizato, M. Kodama, T. Okimoto, K. Mizukami, R. Ogawa, K. Okamoto, M. Shuto, K. Fukuda, Y. Matsui, T. Shimamura, Takanori Hasegawa, Y. Doki, S. Nagayama, K. Yamada, M. Kato, T. Shibata, M. Mori, H. Aburatani, K. Murakami, Y. Suzuki, S. Ogawa, S. Miyano and K. Mimori. A temporal shift of the evolutionary principle shaping intratumor heterogeneity in colorectal cancer. Nature Communications, vol.9(1), p.2884, 2018.
[18] M. Kato, M. Nagai, T. Hasegawa, S. Imoto, S. Matsui, T. Tsunoda and T. Shibata. Comprehensive search for prognostic biomarkers using PCAWG data. Cancer Science, vo.109, 291--291, 2018.
[19] M. Fujita, S. Imoto, R. Yamaguchi, T. Hasegawa, S. Hayashi, K. Kakimi, S. Miyano, H. Yamaue, K. Chayama and H. Nakagawa. Genomic insights into immune suppression in liver cancer. Cancer Science, vol.109, 640-640, 2018.
[20] A. Niida, T. Hasegawa, S. Miyano. Sensitivity analysis of agent-based simulation utilizing massively parallel computation and interactive data visualization. PLOS ONE, vol.14(3), e0210678, 2019.
[21,F11] T. Hasegawa, R. Yamaguchi, A. Niida, S. Miyano and S. Imoto. Ensemble smoothers for inference of hidden states and parameters in combinatorial regulatory model. Journal of the Franklin Institute, Vol.357(5), pp.2916-2933, 2020.
[22] S. Shimizu, J. Mimura, T. Hasegawa, E. Shimizu, S. Imoto, M. Tsushima, S. Kasai, S. Shimizu, H. Yamazaki, Y Ushida, H. Suganuma, H Tomita, M. Yamamoto, S. Nakaji and K. Itoh. Association of single nucleotide polymorphisms in the NRF2 promoter with vascular stiffness with aging. PLOS ONE, vol.15(8), pp.1-17, 2020.
[23] N. Sato, M. Kakuta, E. Uchino, T. Hasegawa, R. Kojima, W. Kobayashi, K. Sawada, Y. Tamura, I. Tokuda, S. Imoto, S. Nakaji, K. Murashita, M. Yanagita and Y. Okuno. The relationship between cigarette smoking and the tongue microbiome in an East Asian population. Journal of Oral Microbiology, vol.12(1), p.1742527, 2020.
[24] A. Niida, T. Hasegawa, H. Innan, T. Shibata, K. Mimori and S. Miyano. A unified simulation model for understanding the diversity of cancer evolution. PeerJ, vol.8, e8842, 2020.
[25,F12] T. Hasegawa, R. Yamaguchi, M. Kakuta, K. Sawada, K. Kawatani, K. Murashita, S. Nakaji and S. Imoto. Prediction of blood test values under different lifestyle scenarios using time-series electronic health record. PLOS ONE, vol.15(3), e0230172, 2020.
[26] The ICGC/TCGA pan-cancer analysis of whole genomes consortium. Pan-cancer analysis of whole genomes. Nature, vol.578, pp.82–93, 2020.
[27] M. Fujita, R. Yamaguchi, T. Hasegawa, S. Shimada, K, Arihiro, S. Hayashi, K. Maejima, K. Nakano, A. Fujimoto, A. Ono, H. Aikata, M. Uenoh, S. Hayami, H. Tanaka, S. Miyano, H. Yamaue, K. Chayama, K. Kakimi, S. Tanakad, S. Imoto and H. Nakagawa. Classification of primary liver cancer with immunosuppression mechanisms and correlation with genomic alterations. EBioMedicine, Vol.54, p.102737, 2020.
[28] N. Sato, M. Kakuta, T. Hasegawa, R. Yamaguchi, E. Uchino, W. Kobayashi, K. Sawada, Y. Tamura, I. Tokuda, K. Murashita, S. Nakaji, S. Imoto, M. Yanagita and Y. Okuno. Metagenomic analysis of bacterial species in tongue microbiome of current and never smokers. Journal of Oral Microbiology, vol.6(11), pp.11, 2020.
[29] A. Fujimoto, M. Fujita, T. Hasegawa, J.H. Wong, K. Maejima, A. Oku-Sasaki, K. Nakano, Y. Shiraishi, S. Miyano, Go Yamamoto, K. Akagi, S. Imoto and H. Nakagawa.Comprehensive analysis of indels in whole-genome microsatellite regions and microsatellite instability across 21 cancer types. Genome Researchm, vol.30, pp.334-346, 2020.
[30,F13] K. Misawa, T. Hasegawa (Equal First), E. Mishima, P. Jutabha, M. Ouchi, K. Kojima, Y. Kawai, M. Matsuo, N. Anzai and M. Nagasaki. Contribution of rare variants of the SLC22A12 gene to the missing heritability of serum urate levels. Genetics, vol.214(4), pp.1079-1090, 2020.
[31] N.Sato, M. Kakuta, T. Hasegawa, R. Yamaguchi, E. Uchino, K. Murashita, S. Nakaji, S. Imoto, M. Yanagita and Y. Okuno. Metagenomic profiling of gut microbiome in early chronic kidney disease. Nephrology, Dialysis Transplantation, gfaa172, 2020.
[32,F14] T. Hasegawa, S. Hayashi, E. Shimizu, S. Mizuno, A. Niida, R. Yamaguchim, S. Miyano, H. Nakagawa and S. Imoto. Neoantimon: A multifunctional R package for identification of tumor-specific neoantigens. Bioinformatics, vol.36(18), pp.4813-4816, 2020.
[33] S. Imoto, T. Hasegawa and R. Yamaguchi. Data science and precision health care, Nutrition reviews, vol.78(Supplement_3), pp.53-57, 2020.
[34] S. Sakimura, S. Nagayama, M. Fukunaga, Q. Hu, A. Kitagawa, Y. Kobayashi, T. Hasegawa, M. Noda, Y. Kouyama, D. Shimizu, T. Saito, A. Niida, Y. Tsuruda, H. Otsu, Y. Matsumoto, H. Uchida, T. Masuda, K. Sugimachi, S. Sasaki, K. Yamada, K. Takahashi, H. Innan, Y. Suzuki, H. Nakamura, Y. Totoki, S. Mizuno, M. Ohshima, T. Shibata and K. Mimori. Tumor immune response determines the postoperative recurrence and detectability of mutated genes in ctDNA in colorectal cancer cases, PLOS Genetics, in press. PLoS genetics, vol.17(1), e1009113, 2021.
[35] COVID-19 Host Genetics Initiative, Mapping the human genetic architecture of COVID-19, Nature, vol.600, 472-477, 2021.
[36] S. Mizuno, R. Yamaguchi, T. Hasegawa, S. Hayashi, M. Fujita, F. Zhang, Y. Koh, S.Y. Lee, S.S. Yoon, E. Shimizu, M. Komura, A. Fujimoto, M. Nagai, M. Kato, H. Liang, S. Miyano, Z. Zhang, H. Nakagawa and S. Imoto. Immunogenomic pan-cancer landscape reveals immune escape mechanisms and immunoediting histories. Scientific Reports, vol.11(1), pp.15713-15713, 2021.
[37] H. Hirata, A. Niida, N. Kakiuchi, R. Uchi, K. Sugimachi, T. Masuda, T. Saito, S. Kageyama, Y. Motomura, S. Ito, T. Yoshitake, D. Tsurumaru, Y. Nishimuta, A. Yokoyama, T. Hasegawa, K. Chiba, Y. Shiraishi, J. Du, F. Miura, M. Morita, Y. Toh, M. Hirakawa, Y. Shioyama, T. Ito, T. Akimoto, S. Miyano, T. Shibata, M. Mori, Y. Suzuki, S. Ogawa, K. Ishigami and K. Mimori. The Evolving Genomic Landscape of Esophageal Squamous Cell Carcinoma Under Chemoradiotherapy. Cancer research, vol.81(19), pp4926-4938, 2021.
[38] T. Hasegawa, R. Yamaguchi, M. Kakuta, M. Ando, J. Songee, I. Tokuda, K. Murashita and S. Imoto. Application of state-space model with skew-t measurement noise to blood test value prediction. Applied Mathematical Modelling, vol.100, pp.365-378, 2021.

Activity

長谷川嵩矩：東京医科歯科大学 M&Dデータ科学センター准教授 (Associate Professor)。東京大学医科学研究所ヒトゲノム解析センター客員准教授。博士(情報学)。経営・技術顧問、アントレプレナーほか

京都大学大学院情報学研究科博士課程の在学中に情報科学技術と芸術の融合を行うべく、CRUNCHERS株式会社を共同創業し、自然言語処理技術を中核とした小説の評価システム(CRUNCH MAGAZINE)と統計的シミュレーションを用いた映画の興行収益予測システム(CRUNCH CINEMA)を開発(前者は2015年1月に事業譲渡)。その後、博士課程を短縮修了して東北大学東北メディカル・メガバンク機構助教に着任し、疾患と遺伝子変異の関連解析並びに希少変異の網羅的かつ高速な解析手法を開発を行う。

2015年に東京大学医科学研究所助教へ着任、複雑な性質を持つ時系列データの内部状態とその将来状態を正確に予測する統計理論研究、ICGCプロジェクトにおける免疫ゲノムデータ解析と解析ライブラリ開発、コホートデータを用いたGWASの層別化解析手法の開発や時系列の検査値・生活習慣データとゲノムデータを統合的に利用することで将来の健康状態を予測する統計科学手法の開発などを行う。一方で、学術研究を社会へ応用すべく、2015年に新興国におけるデータドリブン型医療を志す株式会社miupを共同創業する。同時期に、Medley, Inc.の技術顧問に着任し、ベイズ学習を用いた臨床診断支援システム(Mogul)やインフルエンザの感染予測技術などを開発・提供するほか、パナソニック株式会社の技術顧問として、生産最適化等の業務改善を手掛けるなど、上場企業の技術顧問を歴任する。省庁などにおける委員会や評価会の委員経験も持ち、臨床医学や臨床検査、医療を含む多くの現場における労働法や派遣法の運用に関する知見も持つ。

2020年に東京医科歯科大学M&Dデータ科学センター講師、2021年に同大学准教授に着任。東京大学で行っていた研究に加えて、医科歯科大学内で集められているデータを対象にした生命・健康情報学研究の促進を行っている。現在でも、複数のスタートアップ企業や、株式会社スカラや株式会社キャリアなど上場企業の経営・技術顧問を務めるほか、医療・AIやICT/IoTにこだわらず、飲食、シニア等様々な分野において新規事業の立ち上げを継続して行っている。専門領域にこだわらず、量子計算や暗号理論、生物・情報工学、近代経済論や国際情勢等幅広い分野の知識・技術習得を行っており、ベンチャー企業や新規事業開発におけるデューデリジェンス業務も請け負っている。

Research Bio: Takanori Hasegawa received BS in Engineering from Waseda university, MS in Information Science and Technology from The university of Tokyo, and PhD in Informatics from Kyoto University in 2010, 2012 and 2015, respectively. He is currently an Junior Associate Professor of Integrated Analytics Department, M&D Data Science Center, Tokyo Medical and Dental University. His current research interests cover time series analysis, data assimilation, Bayesian statistical inference, health informatics, genome wide association study, immunological and cancer genome analysis.

CONTACT

Email
t.hasegawa.dsc[at]tmd.ac.jp

SOCIAL LINKS