Computationally-driven Solutions for Healthier Lives (ComSHeL)

Our purpose is to leverage computational and analytical methods to enhance our understanding of health, diseases, treatments, and medical interventions, fostering collaboration within iTHEMS and beyond.

Objectives

From lab experiments in immunology, virology, oncology, to clinical practice, public health and personalised medicine, there is an ever increasing amount of data available to be leveraged by ever more sophisticated approaches, to further human knowledge and health. Researchers in the ComSHeL study group (SG) have a wide range of expertise in the use, development and refinement of analytical and computational methods, including machine learning, AI, mathematical and numerical modelling, task automation, parameter estimation, statistical and causal inference. Our combined expertise has enabled us to make significant advances in the general areas of pandemic threat assessment, oncology, neurodegenerative and infectious diseases, in silico design and optimization of measurement protocols, disease marker identifications, predictive and personalised medicine, differential diagnostics, genealogic reconstruction, etc.

The ComSHeL SG members combined share a broad range of skills that makes them ideally suited to quickly respond and adapt to new research directives, such as TRIP initiatives, to develop computational solutions to tackle challenges to human health and further human knowledge of diseases.

The ComSHeL SG will bring together researchers within iTHEMS and beyond who are interested in research problems aimed at improving our understanding of health, diseases, treatment options, and other medical interventions and practices, to further common research interests. While the main activities of the group will be regular seminars (once every 1-2 weeks), a few workshops per year are expected to bring the three facilitators’ groups together and/or to bring together the SG members with members of another RIKEN laboratory (e.g., CBS, BDR, AIP) or other groups outside RIKEN with a shared interest in disease-related topics, with the aim to stimulate new collaborations or other types of initiatives.

Facilitators:: Catherine Beauchemin (RIKEN iTHEMS) *Contact at cbeau@riken.jp; Jun Seita (Medical Data Deep Learning Team (ADSP), RIKEN); Eiryo Kawakami (Medical Data Deep Learning Team (ADSP), RIKEN)