Effective utilization of rapidly developing ‘omic profiling technologies and 55the introduction of personalized/precision/preventive medicine have re-cently become major goals of biomedical research. This paradigm shift requires moving away from traditional approaches that do not adequately consider the individual characteristics of each patient. Our laboratory develops new strate-gies to address these challenges by bringing ideas and methods from mathemat-ics and computational sciences to the medical domain. The first part of our approach is driven by integrative analysis of clinical and omic data and aims to explore the etiologies of intractable diseases. Next, we classify each disease into finer categories, such as based on the types of anti-cancer immune responses, using molecular profiles, and then clarify the underlying causal mechanisms with systems-based approaches. Lastly, we apply mathematical and machine learning techniques to infer optimal therapy for each patient to guide treat-ment decisions made by their hospital or clinic. Similar approaches can be used for disease prevention based on an individual’s medical history. Our research projects include: (1) Investigating the relationship between tumor microen-vironment, subclonal diversity, drug response, and patient prognosis in lung, colorectal and liver cancer, (2) Development of novel machine learning meth-ods for cancer immunology multi-omics, (3) Integrative trans-omics modelling of disease-associated genomic variations, (4) Accurate insertion/deletion call-ing from next-generation sequencing (NGS) data, (5) Whole exome sequencing (WES) analysis to identify intractable disease-causing genes, (6) Cancer whole genome sequencing (WGS) analysis, (7) Development of new clustering meth-ods, (8) Development of cancer classification and prognosis prediction methods based on gene expression data, (9) Prediction of optimal drug combinations for cancer chemotherapy, (10) Drug toxicity prediction with machine learning, (11) Prediction of post-translational amino-acid modifications, protein struc-ture, protein-peptide interactions, molecular recognition features (MoRFs), and protein functions, (12) Discovery of clinically-relevant subtypes for cancer immunotherapy, and (13) Development of explainable AI and deep learning technologies for image and ‘omic data analyses.Figure: DeepFeature – deep learning for ‘omic data analysis and interpretationThe overall DeepFeature procedure for feature selection using a convolutional neural network.Recent Major PublicationsSharma A, Lysenko A, Boroevich KA, Vans E, Tsunoda T. DeepFeature: feature selection in nonimage data using convolutional neural network. Brief Bioinform 22, bbab297 (2021)Rheinbay E, Nielsen MM, Abascal F, Wala JA, Shapira O, Tiao G, Hornshøj H, Hess JM, Juul RI, Lin Z, Feuerbach L, Sabarinathan R, Madsen T, Kim J, Mularoni L, Shuai S, Lanzós A, Herrmann C, Maruvka YE, Shen C, Amin SB, Bandopadhayay P, Bertl J, Boroevich KA, Busanovich J, Carlevaro-Fita J, Chakravarty D, Chan CWY, Craft D, Dhingra P, Diamanti K, Fonseca NA, Gonzalez-Perez A, Guo Q, Hamilton MP, Haradhvala NJ, Hong C, Isaev K, Johnson TA, ... , Tsunoda T, et al. Analyses of non-coding somatic drivers in 2,658 cancer whole genomes. Nature 578, 102-111 (2020)Nishino J, Watanabe S, Miya F, Kamatani T, Sugawara T, Boroevich KA, Tsunoda T. Quantification of multicellular colonization in tumor metastasis using exome sequenc-ing data. Int J Cancer 146, 2488-2497 (2020)Invited presentationsTsunoda T. “Deep Learning and Mathematical Sciences for Advancing Genomic Medicine” The 25th Annual Meeting of the Japanese Association for Molecular Tar-get Therapy of Cancer, Symposium 2 “AI” (Japan/Online) May 2021Tsunoda T. “Genomic Medicine with New Intelligence” The University of Tokyo Institute for Genomic Medicine 2020 Symposium (Japan/Online) March 2021Tsunoda T. “Exploring etiologies, sub-classification, and risk prediction of diseases based on big-data analysis of clinical and whole omics data in medicine” ERCIM-JST Joint Symposium on Big Data and Artificial Intelligence (Japan/Online) February 2021Tsunoda T. “Exploring etiologies, sub-classification, and risk prediction of diseases based on big-data analysis of clinical and whole omics data in medicine” CREST Inter-national Symposium on Big Data Application (Japan/Online) January 2021Tsunoda T. “Deep learning opens up new frontiers in genomic medicine” The 16th The Japanese Association for Molecular Target Therapy of Cancer TR Workshop (Japan/Online) January 2021Laboratory for Medical Science MathematicsTeam Leader: Tatsuhiko Tsunoda
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