Welcome to Koyama Lab
Leveraging expertise in AI, quantum computing, and biology, we are thrilled to deepen our comprehension of science of microbiome by deciphering dark matter in metabolomics and unravelling uncharted roles of virome with preemptive applications of quantum machine learning as quantum computing ushers in a new paradigm.
Job Opening for postdocs
We are seeking a highly motivated and innovative Interdisciplinary Research Scientist to join our cutting-edge laboratory at the intersection of Artificial Intelligence, Quantum Computing, and Life Sciences. The ideal candidate will have a strong background in AI and machine learning, or demonstrable experience in quantum computing algorithms. Additionally, expertise in either organic chemistry or biology is required to bridge the gap between computational methods and life sciences applications. This unique position offers the opportunity to work on groundbreaking projects in microbiome research and development of novel quantum algorithms for simulating complex biological systems. We welcome candidates with a Ph.D. in Computer Science, Bioinformatics, Physics, Mathematics, Chemistry, Biology, or Neuroscience who have a passion for interdisciplinary work and a seasoned problem solving skill. Please, send us
- CV
- List of research activities, including full list of publications, achievements, etc.
- Motivation letter including a description of how you plan to contribute to Bio2Q.
- Recommendation letter(s)
News
- 2025/05/01: Interview by DailySun New York (in Japanse)
- 2025/05/20: Introduction to AI #1 by DailySun New York (in Japanse)
Projects
Our current research projects include:
- metabolite predictor: predicting metabolites from genomic profiles of bacteria.
- genome language model for bacteria to discover bacteria's vital genes and regulatory mechanisms.
- Developing quantum machine learning algorithms for Noisy Intermediate-Scale Quantum Computer (NISQ) and near term Fault Tolerant Quantum Computer (FTQC).
- AI in Neuroscience
Publications
Recent publications with NotebookLM generated deep dive:
| Koyama, T., et al. (2022). "Evasion of vaccine-induced humoral immunity by emerging sub-variants of SARS-CoV-2." Future Microbiol 17: 417-424. DOI: 10.2217/fmb-2022-0025 |
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| Koyama, T., et al. (2022). "Cross-Border Transmissions of the Delta Substrain AY.29 During Tokyo Olympic and Paralympic Games." Front Microbiol 13: 883849. DOI: 10.3389/fmicb.2022.883849 |
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| Miyakawa, K., et al. (2022). "Molecular and Epidemiological Characterization of Emerging Immune-Escape Variants of SARS-CoV-2." Front Med (Lausanne) 9: 811004. DOI: 10.3389/fmed.2022.811004 |
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| Koyama, T., et al. (2020). "Emergence of Drift Variants That May Affect COVID-19 Vaccine Development and Antibody Treatment." Pathogens 9(5). DOI: 10.3390/pathogens9050324 |
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| Tokumasu, R., et al. (2021). "Introductions and evolutions of SARS-CoV-2 strains in Japan." medRxiv: 2021.2002.2026.21252555. DOI: 10.1101/2021.02.26.21252555 |
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| Koyama, T., et al. (2020). "Variant analysis of SARS-CoV-2 genomes." Bull World Health Organ 98(7): 495-504. DOI: 10.2471/BLT.20.253591 |
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| Koyama, T., et al. (2019). "Analysis on GENIE reveals novel recurrent variants that affect molecular diagnosis of sizable number of cancer patients." BMC Cancer 19(1): 114. DOI: 10.1186/s12885-019-5313-1 |
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| Frank, M. O., Koyama, T. et al. (2019). "Sequencing and curation strategies for identifying candidate glioblastoma treatments." BMC Medical Genomics 12(1): 56. DOI: 10.1186/s12920-019-0500-0 |
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| Itahashi, K., et al. (2018). "Evaluating Clinical Genome Sequence Analysis by Watson for Genomics." Front Med (Lausanne) 5: 305. DOI: 10.3389/fmed.2018.00305 |
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| Patel, N. M., et al. (2017). "Enhancing Next-Generation Sequencing-Guided Cancer Care Through Cognitive Computing." Oncologist. DOI: 10.1634/theoncologist.2017-0170 |
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| Wrzeszczynski, K. O., Frank, M. O., Koyama, T.,et al. (2017). "Comparing sequencing assays and human-machine analyses in actionable genomics for glioblastoma." Neurol Genet 3(4): e164. DOI: 10.1212/nxg.0000000000000164 |
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Lab Members
Prof. Takahiko Koyama - Principal Investigator, Bio2Q, Keio University
At Takeda, I engaged in optimizing lead compounds in numerous drug discovery projects, each addressing critical aspects of cancer or central nervous system disorders. In particular, I was focusing on allosteric inhibitors against kinases aiming higher affinities with high specificity.
During my tenure at IBM Watson Research Center, I paved the way for genomic medicine, laying the groundwork for Watson for Genomics by leading scientists across the globe. In 2020, my paper titled 'Variant Analysis of SARS-CoV-2 Genome' stands as early yet influential contribution to comprehending the pernicious virus responsible for COVID-19.
- 2014-2024: Research Staff Member, IBM TJ Watson Research Center
- 2007-2014: Tokyo Research Laboratory, IBM Japan
- 2003-2007: Senior Researcher, Takeda Pharmaceutical Company
- 2001-2003: Postdoctral Fellow, School of Medicine, University of Pennsylvania
- 1999-2001: Oracle Japan
- 1994-1999: Ph.D. Candidate, Physics Dept., Cornell University
Dr. Ashish Joshi - Postdoctral Researcher, Bio2Q, Keio University
I am a condensed matter physicist with research interests in computational approaches to solve
scientific problems. During my Ph.D., I used machine learning methods to study quantum
many-body systems. At Koyama lab, I am developing machine learning and quantum computing
techniques for biology. While machine learning is already making huge strides in various fields,
when combined with quantum computing it has the potential to revolutionize the field of scientific
computing. We are working with the experts in various fields to bridge the gap between these
disparate research fields.
- 2021-2024: Ph.D.Candidate, Dept. of Physics, Kyoto University
- 2019-2021: Master of Science Candidate, Dept. of Physics, Kyoto University
- 2014-2018: Undergraduate Student, Applied Physics Dept., Delhi Technological University, India
Contact
Email: takahiko.koyamaabcdefghij@keio.jp
Phone: +81-3-5363-3460
Address:35 Shinanomachii, Research Park 1N8, Shinjuku-ku , Tokyo 160-8582, Japan