He is an Assistant Professor at The University of Tokyo, a Co-founder at miup Inc., and a Technical Advisor at Panasonic, Inc..
He obtained his Ph.D. on statistical time series analysis and computational biology 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 to a team that was developing a medical disease inference system using Bayesian learning and an influenza-infection prediction system based on time-series data.
He was appointed to his current position in 2015, where he has four key focus areas. First, he is 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 focuses on cancer-immunogenomics and the development of their analysis tools in the International Cancer Genome Consortium. Third, he handles the stratification analysis of GWAS using cohort data, and lastly, he is 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 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.
He has also served as a technical advisor on production and experiment optimization to several large companies. Further, he has guided the development of AI systems (statistical science and machine learning) at startups. Especially in miup Inc. from the University of Tokyo, he formulates the business strategy and oversees progress management, capital policy, and foreign negotiations. He has handled the business right from the inception of the company, well into its growth phase.
Department of Intelligence Science and Technology
Graduate School of Informatics, Kyoto University
(Bioinformatics Center, Institute for Chemical Research, Kyoto University)
Thesis Title: Reconstructing Biological Systems Incorporating Multi-Source Biological Data via Data Assimilation Techniques
Department of Computer Science
Graduate School of Information Science, The University of Tokyo
(Human Genome Center, Institute of Medical Science, The University of Tokyo)
Thesis Title: Intracellular Systems Analysis by Assimilating Large Scale Biological Data and Pathway Simulation
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
Our 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.
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.
Detail is Confidential: Production and experiment optimization of an image processing devision based on Bayesian framework.
Confidential: Advising labor laws and dispatch law operations at medical sites.
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.
Confidential: Education for A.I. development especially for image processing.
Confidential: Consultation of data science and A.I. business.
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.
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.
Japan Society for The Promotion of Science
The research topic was Reconstructing Biological Systems Incorporating Multi-Source Biological Data via Data Assimilation Techniques.
[J1] 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.
[J2] 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.
[J3] 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.
[J4] 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), 2015.
[J5] 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.
[J6] J. Kurashige, T. Hasegawa (Equal First), et al. Integrated molecular profiling of human gastric cancer identifies DDR2 as a potential regulator of peritoneal dissemination. Scientific Reports, vol.6, pp.22371, 2016.
[J7] 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.
[J8] 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. BMC Genomics (11th The International Symposium on Bioinformatics Research and Applications), vol.17(Suppl. 5), pp.465-476, 2016.
[J9] S. Koshiba, Y. Yamaguchi, K. Kojima, T. Hasegawa, et al. The structural origin of metabolic quantitative diversity. Scientific Reports, vol.6, pp.31463, 2016.
[J10] 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.
[J11] T. Morita, A. Rahman, T. Hasegawa, A. Ozaki, and T. Tanimoto. The Potential Possibility of Symptom Checker, International Journal of Health Policy and Management, 2017.
[J12] T. Saito, ..., T. Hasegawa, et al. A temporal shift of the evolutionary principle shaping intratumor heterogeneity in colorectal cancer, Nature Communications, vol.9(1), pp.2884, 2018.
[J13] 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), pp6148-6161, 2018.
[C1] T. Hasegawa, R. Yamaguchi, M. Nagasaki, S. Imoto and S. Miyano. Poster: Comprehensive pharmacogenomic pathway screening by data assimilation, Proc. Computational Advances in Bio and Medical Sciences (ICCABS), 2011 IEEE 1st International Conference on, pp.246-246, 2011.
[C2] T. Hasegawa, R. Yamaguchi, M. Nagasaki, S. Imoto and S. Miyano. Comprehensive pharmacogenomic pathway screening by data assimilation, Proc. 7th International Symposium on Bioinformatics Research and Applications, volume 6674 of Lecture Notes in Computer Science, pp.160-171. Springer Berlin Heidelberg, 2011.
[C3] 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. Proc. 12th International Symposium on Bioinformatics Research and Applications, 2016.
長谷川嵩矩：東京大学医科学研究所 ヘルスインテリジェンスセンター 助教。博士(情報学)。
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。また同時期からMedley, Inc.の技術顧問に着任し、ベイズ学習を用いた医療用疾患推定システム(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。
また、大企業の生産及び実験最適化やスタートアップ企業のAI (統計科学、機械学習) 開発に関する技術顧問の経験も有している。特に東大発ベンチャー企業であるmiup社では共同創業者として事業戦略の策定、複数事業の進捗管理、資本政策並びに対外交渉も一手に担っており、創業期から成長期にかけてのビジネス経験も厚い。
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 Assistant Professor of Health Intelligence Center, Institute of Medical Science, University of Tokyo. His current research interests cover time series analysis, data assimilation, Bayesian statistical inference, health informatics, genome wide association study, immunological and cancer genome analysis.