AA
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
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
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
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.
Human Genome Center, The Institute of Medical Science, The University of Tokyo
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.
CAREER CO., LTD
Detail is Confidential: Development of new business, and Operation and Production optimization.
Scala Group
Detail is Confidential: Development of new business and Evaluation of start-up companies.
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.
Health Intelligence Center, The Institute of Medical Science, The University of Tokyo
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.
Panasonic Inc.
Detail is Confidential: Production and experiment optimization of an image processing devision based on Bayesian framework.
Nadia inc.
Detail is Confidential: Education for A.I. development especially for image processing.
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.
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.
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.
Japan Society for The Promotion of Science
The research topic was Reconstructing Biological Systems Incorporating Multi-Source Biological Data via Data Assimilation Techniques.
[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.
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.