| POODLE-L: a two-level SVM prediction system for reliably predicting long disordered regions S Hirose, K Shimizu, S Kanai, Y Kuroda, T Noguchi Bioinformatics 23 (16), 2046-2053, 2007 | 171 | 2007 |
| POODLE-S: web application for predicting protein disorder by using physicochemical features and reduced amino acid set of a position-specific scoring matrix K Shimizu, S Hirose, T Noguchi Bioinformatics 23 (17), 2337-2338, 2007 | 130 | 2007 |
| ANGLE: a sequencing errors resistant program for predicting protein coding regions in unfinished cDNA K Shimizu, J Adachi, Y Muraoka Journal of Bioinformatics and Computational Biology 4 (03), 649-664, 2006 | 124 | 2006 |
| Efficient privacy-preserving string search and an application in genomics K Shimizu, K Nuida, G Rätsch Bioinformatics 32 (11), 1652-1661, 2016 | 98 | 2016 |
| Interaction between intrinsically disordered proteins frequently occurs in a human protein–protein interaction network K Shimizu, H Toh Journal of molecular biology 392 (5), 1253-1265, 2009 | 76 | 2009 |
| Predicting mostly disordered proteins by using structure-unknown protein data K Shimizu, Y Muraoka, S Hirose, K Tomii, T Noguchi BMC bioinformatics 8, 1-15, 2007 | 74 | 2007 |
| Differentially private bayesian learning on distributed data M Heikkilä, E Lagerspetz, S Kaski, K Shimizu, S Tarkoma, A Honkela Advances in neural information processing systems 30, 2017 | 71 | 2017 |
| PoSSuM: a database of similar protein–ligand binding and putative pockets JI Ito, Y Tabei, K Shimizu, K Tsuda, K Tomii Nucleic acids research 40 (D1), D541-D548, 2012 | 69 | 2012 |
| Search system, search method, and program K Iwamura, T Hirokawa, K Tsuda, H Arai, J Sakuma, K Asai, M Hamada, ... US Patent 9,215,068, 2015 | 37 | 2015 |
| Differentially private cross-silo federated learning MA Heikkilä, A Koskela, K Shimizu, S Kaski, A Honkela arXiv preprint arXiv:2007.05553, 2020 | 36 | 2020 |
| Privacy-preserving string search for genome sequences with fhe bootstrapping optimization Y Ishimaki, H Imabayashi, K Shimizu, H Yamana 2016 IEEE International Conference on Big Data (Big Data), 3989-3991, 2016 | 36 | 2016 |
| PDB‐scale analysis of known and putative ligand‐binding sites with structural sketches JI Ito, Y Tabei, K Shimizu, K Tomii, K Tsuda Proteins: Structure, Function, and Bioinformatics 80 (3), 747-763, 2012 | 36 | 2012 |
| POODLE-I: disordered region prediction by integrating POODLE series and structural information predictors based on a workflow approach S Hirose, K Shimizu, T Noguchi In silico biology 10 (3-4), 185-191, 2010 | 35 | 2010 |
| Efficient two-level homomorphic encryption in prime-order bilinear groups and a fast implementation in webassembly N Attrapadung, G Hanaoka, S Mitsunari, Y Sakai, K Shimizu, T Teruya Proceedings of the 2018 on Asia Conference on Computer and Communications …, 2018 | 31 | 2018 |
| SlideSort: all pairs similarity search for short reads K Shimizu, K Tsuda Bioinformatics 27 (4), 464-470, 2011 | 31 | 2011 |
| Privacy-preserving search for chemical compound databases K Shimizu, K Nuida, H Arai, S Mitsunari, N Attrapadung, M Hamada, ... BMC bioinformatics 16, 1-14, 2015 | 23 | 2015 |
| Secure wavelet matrix: Alphabet-friendly privacy-preserving string search for bioinformatics H Sudo, M Jimbo, K Nuida, K Shimizu IEEE/ACM transactions on computational biology and bioinformatics 16 (5 …, 2018 | 19* | 2018 |
| Discovery of cryoprotective activity in human genome-derived intrinsically disordered proteins N Matsuo, N Goda, K Shimizu, S Fukuchi, M Ota, H Hiroaki International journal of molecular sciences 19 (2), 401, 2018 | 16 | 2018 |
| A method for systematic assessment of intrinsically disordered protein regions by NMR N Goda, K Shimizu, Y Kuwahara, T Tenno, T Noguchi, T Ikegami, M Ota, ... International journal of molecular sciences 16 (7), 15743-15760, 2015 | 14 | 2015 |
| SAHG, a comprehensive database of predicted structures of all human proteins C Motono, J Nakata, R Koike, K Shimizu, M Shirota, T Amemiya, K Tomii, ... Nucleic acids research 39 (suppl_1), D487-D493, 2011 | 14 | 2011 |
