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Sayaka Mizutani
Researcher at Tokyo Institute of Technology
Publications - 27
Citations - 2269
Sayaka Mizutani is an academic researcher from Tokyo Institute of Technology. The author has contributed to research in topics: Microbiome & Medicine. The author has an hindex of 12, co-authored 17 publications receiving 1231 citations. Previous affiliations of Sayaka Mizutani include Japan Society for the Promotion of Science & Kyoto University.
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Journal ArticleDOI
Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer
Jakob Wirbel,Paul Theodor Pyl,Paul Theodor Pyl,Ece Kartal,Konrad Zych,Alireza Kashani,Alessio Milanese,Jonas S. Fleck,Anita Y. Voigt,Albert Pallejà,Ruby Ponnudurai,Shinichi Sunagawa,Luis Pedro Coelho,Petra Schrotz-King,Emily Vogtmann,Nina Habermann,Emma Niméus,Andrew Maltez Thomas,Andrew Maltez Thomas,Paolo Manghi,Sara Gandini,Davide Serrano,Sayaka Mizutani,Sayaka Mizutani,Hirotsugu Shiroma,Satoshi Shiba,Tatsuhiro Shibata,Shinichi Yachida,Takuji Yamada,Takuji Yamada,Levi Waldron,Alessio Naccarati,Nicola Segata,Rashmi Sinha,Cornelia M. Ulrich,Hermann Brenner,Manimozhiyan Arumugam,Manimozhiyan Arumugam,Peer Bork,Georg Zeller +39 more
TL;DR: A meta-analysis of eight geographically and technically diverse fecal shotgun metagenomic studies of colorectal cancer identified a core set of 29 species significantly enriched in CRC metagenomes, establishing globally generalizable, predictive taxonomic and functional microbiome CRC signatures as a basis for future diagnostics.
Journal ArticleDOI
Metagenomic and metabolomic analyses reveal distinct stage-specific phenotypes of the gut microbiota in colorectal cancer
Shinichi Yachida,Sayaka Mizutani,Hirotsugu Shiroma,Satoshi Shiba,Takeshi Nakajima,Taku Sakamoto,Hikaru Watanabe,Keigo Masuda,Yuichiro Nishimoto,Masaru Kubo,Fumie Hosoda,Hirofumi Rokutan,Minori Matsumoto,Hiroyuki Takamaru,Masayoshi Yamada,Takahisa Matsuda,Motoki Iwasaki,Taiki Yamaji,Tatsuo Yachida,Tomoyoshi Soga,Ken Kurokawa,Atsushi Toyoda,Yoshitoshi Ogura,Tetsuya Hayashi,Masanori Hatakeyama,Hitoshi Nakagama,Yutaka Saito,Shinji Fukuda,Tatsuhiro Shibata,Takuji Yamada,Takuji Yamada +30 more
TL;DR: Large-cohort multi-omics data indicate that shifts in the microbiome and metabolome occur from the very early stages of the development of colorectal cancer, which is of possible etiological and diagnostic importance.
Journal ArticleDOI
Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation
Andrew Maltez Thomas,Andrew Maltez Thomas,Paolo Manghi,Francesco Asnicar,Edoardo Pasolli,Federica Armanini,Moreno Zolfo,Francesco Beghini,Serena Manara,Nicolai Karcher,Chiara Pozzi,Sara Gandini,Davide Serrano,Sonia Tarallo,Antonio Francavilla,Gaetano Gallo,Mario Trompetto,Giulio Ferrero,Sayaka Mizutani,Sayaka Mizutani,Hirotsugu Shiroma,Satoshi Shiba,Tatsuhiro Shibata,Shinichi Yachida,Takuji Yamada,Takuji Yamada,Jakob Wirbel,Petra Schrotz-King,Cornelia M. Ulrich,Hermann Brenner,Manimozhiyan Arumugam,Manimozhiyan Arumugam,Peer Bork,Georg Zeller,Francesca Cordero,Emmanuel Dias-Neto,João C. Setubal,João C. Setubal,Adrian Tett,Barbara Pardini,Maria Rescigno,Levi Waldron,Alessio Naccarati,Nicola Segata +43 more
TL;DR: The combined analysis of heterogeneous CRC cohorts identified reproducible microbiome biomarkers and accurate disease-predictive models that can form the basis for clinical prognostic tests and hypothesis-driven mechanistic studies.
Journal ArticleDOI
Relating drug–protein interaction network with drug side effects
TL;DR: A large-scale analysis to extract correlated sets of targeted proteins and side effects, based on the co-occurrence of drugs in protein-binding profiles and side effect profiles, using sparse canonical correlation analysis is performed.
Journal ArticleDOI
Systematic Drug Repositioning for a Wide Range of Diseases with Integrative Analyses of Phenotypic and Molecular Data
TL;DR: A new computational method to predict unknown drug indications for systematic drug repositioning in a framework of supervised network inference and shows that the proposed method outperforms previous methods in terms of accuracy and applicability, and its performance does not depend on drug chemical structure similarity.