| MetaVelvet: an extension of Velvet assembler to de novo metagenome assembly from short sequence reads T Namiki, T Hachiya, H Tanaka, Y Sakakibara Proceedings of the 2nd ACM conference on bioinformatics, computational …, 2011 | 742 | 2011 |
| Stochastic context-free grammers for tRNA modeling Y Sakakibara, M Brown, R Hughey, IS Mian, K Sjölander, RC Underwood, ... Nucleic acids research 22 (23), 5112-5120, 1994 | 553 | 1994 |
| Efficient learning of context-free grammars from positive structural examples Y Sakakibara Information and Computation 97 (1), 23-60, 1992 | 286 | 1992 |
| Learning context-free grammars from structural data in polynomial time Y Sakakibara Theoretical Computer Science 76 (2-3), 223-242, 1990 | 243 | 1990 |
| Recent advances of grammatical inference Y Sakakibara Theoretical Computer Science 185 (1), 15-45, 1997 | 242 | 1997 |
| RNA secondary structure prediction using deep learning with thermodynamic integration K Sato, M Akiyama, Y Sakakibara Nature communications 12 (1), 941, 2021 | 221 | 2021 |
| Chickens possess centromeres with both extended tandem repeats and short non-tandem-repetitive sequences WH Shang, T Hori, A Toyoda, J Kato, K Popendorf, Y Sakakibara, ... Genome research 20 (9), 1219-1228, 2010 | 187 | 2010 |
| Convolutional neural network based on SMILES representation of compounds for detecting chemical motif M Hirohara, Y Saito, Y Koda, K Sato, Y Sakakibara BMC bioinformatics 19, 83-94, 2018 | 186 | 2018 |
| Generation of a nonhuman primate model of severe combined immunodeficiency using highly efficient genome editing K Sato, R Oiwa, W Kumita, R Henry, T Sakuma, R Ito, R Nozu, T Inoue, ... Cell stem cell 19 (1), 127-138, 2016 | 160 | 2016 |
| Genome sequence and analysis of the Japanese morning glory Ipomoea nil A Hoshino, V Jayakumar, E Nitasaka, A Toyoda, H Noguchi, T Itoh, ... Nature communications 7 (1), 13295, 2016 | 157 | 2016 |
| Whole genome assembly of a natto production strain Bacillus subtilis natto from very short read data Y Nishito, Y Osana, T Hachiya, K Popendorf, A Toyoda, A Fujiyama, ... BMC genomics 11, 1-12, 2010 | 126 | 2010 |
| Statistical prediction of protein–chemical interactions based on chemical structure and mass spectrometry data N Nagamine, Y Sakakibara Bioinformatics 23 (15), 2004-2012, 2007 | 126 | 2007 |
| RNA secondary structural alignment with conditional random fields K Sato, Y Sakakibara Bioinformatics 21 (suppl_2), ii237-ii242, 2005 | 125 | 2005 |
| MetaVelvet-SL: an extension of the Velvet assembler to a de novo metagenomic assembler utilizing supervised learning Afiahayati, K Sato, Y Sakakibara DNA research 22 (1), 69-77, 2015 | 117 | 2015 |
| Chaperone therapy for neuronopathic lysosomal diseases: competitive inhibitors as chemical chaperones for enhancement of mutant enzyme activities Y Suzuki, S Ogawa, Y Sakakibara Perspectives in Medicinal Chemistry 3, PMC. S2332, 2009 | 111 | 2009 |
| Grammatical inference in bioinformatics Y Sakakibara IEEE Transactions on Pattern Analysis and Machine Intelligence 27 (7), 1051-1062, 2005 | 110 | 2005 |
| Building of a document classification tree by recursive optimization of keyword selection function Y Sakakibara, K Misue US Patent 5,463,773, 1995 | 109 | 1995 |
| Pair stochastic tree adjoining grammars for aligning and predicting pseudoknot RNA structures H Matsui, K Sato, Y Sakakibara Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference, 2004 …, 2004 | 107 | 2004 |
| Comprehensive evaluation of non-hybrid genome assembly tools for third-generation PacBio long-read sequence data V Jayakumar, Y Sakakibara Briefings in bioinformatics 20 (3), 866-876, 2019 | 104 | 2019 |
| Convolutional neural networks for classification of alignments of non-coding RNA sequences G Aoki, Y Sakakibara Bioinformatics 34 (13), i237-i244, 2018 | 85 | 2018 |
