Kengo Sato

发表

A max-margin training of RNA secondary structure prediction integrated with the thermodynamic model

Yasubumi Sakakibara, Manato Akiyama, Kengo Sato, 2017, bioRxiv.

Sequence and Structural Analyses for Functional Non-coding RNAs

Yasubumi Sakakibara, Kengo Sato, Y. Sakakibara, 2009, Algorithmic Bioprocesses.

Gradient-based optimization of hyperparameters for base-pairing profile local alignment kernels.

Yasubumi Sakakibara, Kengo Sato, Yutaka Saito, 2009, Genome informatics. International Conference on Genome Informatics.

Improved prediction of transcription binding sites from chromatin modification data

Paul Horton, Timothy L. Bailey, Tom Whitington, 2010, 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology.

Prediction of Gene Structures from RNA-seq Data Using Dual Decomposition

Yasubumi Sakakibara, Kengo Sato, Tatsumu Inatsuki, 2015 .

Predictions of RNA secondary structure by combining homologous sequence information

Kiyoshi Asai, Michiaki Hamada, Hisanori Kiryu, 2009, Bioinform..

Endogenous Bioactive Peptides as Potential Biomarkers for Atherosclerotic Coronary Heart Disease

Kengo Sato, Takuya Watanabe, Fumiko Itoh, 2012, Sensors.

CentroidHomfold-LAST: accurate prediction of RNA secondary structure using automatically collected homologous sequences

Koichiro Yamada, Kiyoshi Asai, Martin C. Frith, 2011, Nucleic Acids Res..

Convolutional neural network based on SMILES representation of compounds for detecting chemical motif

Yutaka Saito, Yasubumi Sakakibara, Kengo Sato, 2018, BMC Bioinformatics.

Improvement of Structure Conservation Index with Centroid Estimators

Yasubumi Sakakibara, Kengo Sato, Yohei Okada, 2010, Pacific Symposium on Biocomputing.

A Non-parametric Bayesian Approach for Predicting RNA Secondary Structures

Kiyoshi Asai, Michiaki Hamada, Yasubumi Sakakibara, 2009, WABI.

Automatic Extraction of Linky Strings in Natural Languages

Masakazu Nakanishi, Shiho Nobesawa, Kengo Sato, 1996 .

A Machine Learning Based Approach to de novo Sequencing of Glycans from Tandem Mass Spectrometry Spectrum

Yasubumi Sakakibara, Kengo Sato, Shotaro Kumozaki, 2015, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

CentroidAlign: fast and accurate aligner for structured RNAs by maximizing expected sum-of-pairs score

Kiyoshi Asai, Michiaki Hamada, Hisanori Kiryu, 2009, Bioinform..

Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications

Satoshi Kobayashi, Tetsuro Nishino, Etsuji Tomita, 2006 .

IPknot: fast and accurate prediction of RNA secondary structures with pseudoknots using integer programming

Tatsuya Akutsu, Kiyoshi Asai, Michiaki Hamada, 2011, Bioinform..

Segmenting Sentences into Linky Strings Using D-bigram Statistics

Masakazu Nakanishi, Shiho Nobesawa, Kengo Sato, 1996, COLING.

An efficient algorithm for de novo predictions of biochemical pathways between chemical compounds

Yutaka Saito, Yasubumi Sakakibara, Kengo Sato, 2012, BMC Bioinformatics.

Using binding Profiles to Predict binding sites of Target RNAs

Tatsuya Akutsu, Kengo Sato, Yuki Kato, 2011, J. Bioinform. Comput. Biol..

A max-margin training of RNA secondary structure prediction integrated with the thermodynamic model

Yasubumi Sakakibara, Manato Akiyama, Kengo Sato, 2017 .

CentroidFold: a web server for RNA secondary structure prediction

Kiyoshi Asai, Michiaki Hamada, Kengo Sato, 2009, Nucleic Acids Res..

Preferential Presentation of Japanese Near-synonyms using Definition Statements

Hiroaki Saito, Kengo Sato, Hiroyuki Okamoto, 2003, IWP@ACL.

Robust and accurate prediction of noncoding RNAs from aligned sequences

Yutaka Saito, Yasubumi Sakakibara, Kengo Sato, 2010, BMC Bioinformatics.

Maximum Entropy Model Learning of the Translation Rules

Masakazu Nakanishi, Kengo Sato, 1998, COLING-ACL.

Extracting Word Sequence Correspondences Based on Support Vector Machines.

Hiroaki Saito, Kengo Sato, 2003 .

A New E-Learning Paradigm through Annotating Operations

Kyoko Ohara, Hiroaki Saito, Kengo Sato, 2004 .

Pair Stochastic Tree Adjoining Grammars for Aligning and Predicting Pseudoknot RNA Structures

Hiroshi Matsui, Yasubumi Sakakibara, Kengo Sato, 2004, CSB.

Introduction to Selected Papers from GIW2018

Limsoon Wong, Yun Zheng, Kenta Nakai, 2018, J. Bioinform. Comput. Biol..

Directed acyclic graph kernels for structural RNA analysis

Kiyoshi Asai, Yasubumi Sakakibara, Kengo Sato, 2008, BMC Bioinformatics.

Fast and accurate clustering of noncoding RNAs using ensembles of sequence alignments and secondary structures

Yutaka Saito, Yasubumi Sakakibara, Kengo Sato, 2011, BMC Bioinformatics.

Prediction of RNA secondary structure using generalized centroid estimators

Kiyoshi Asai, Michiaki Hamada, Hisanori Kiryu, 2009, Bioinform..

DAFS: simultaneous aligning and folding of RNA sequences via dual decomposition

Tatsuya Akutsu, Kiyoshi Asai, Yasubumi Sakakibara, 2012, Bioinform..

An accessibility-incorporated method for accurate prediction of RNA–RNA interactions from sequence data

Tatsuya Akutsu, Tomoya Mori, Kengo Sato, 2017, Bioinform..

Software.ncrna.org: web servers for analyses of RNA sequences

Yasuo Tabei, Hiroshi Matsui, Kiyoshi Asai, 2008, Nucleic Acids Res..

Prediction of RNA secondary structure by maximizing pseudo-expected accuracy

Kiyoshi Asai, Michiaki Hamada, Kengo Sato, 2010, BMC Bioinformatics.

RactIP: fast and accurate prediction of RNA-RNA interaction using integer programming

Tatsuya Akutsu, Kiyoshi Asai, Michiaki Hamada, 2010, Bioinform..

RNA secondary structural alignment with conditional random fields

Yasubumi Sakakibara, Kengo Sato, Y. Sakakibara, 2005, ECCB/JBI.

SHARAKU: an algorithm for aligning and clustering read mapping profiles of deep sequencing in non-coding RNA processing

Yasubumi Sakakibara, Kengo Sato, Masaya Abe, 2016, Bioinform..

A Non-Parametric Bayesian Approach for Predicting RNA Secondary Structures

Kiyoshi Asai, Michiaki Hamada, Yasubumi Sakakibara, 2009, J. Bioinform. Comput. Biol..

Pair stochastic tree adjoining grammars for aligning and predicting pseudoknot RNA structures

Yasubumi Sakakibara, Kengo Sato, Hiroshi Matsui, 2004 .

Improving the accuracy of predicting secondary structure for aligned RNA sequences

Kiyoshi Asai, Michiaki Hamada, Kengo Sato, 2010, Nucleic Acids Res..

COPICAT: a software system for predicting interactions between proteins and chemical compounds

Kris Popendorf, Takashi Komori, Yasubumi Sakakibara, 2012, Bioinform..

Stem Kernels for RNA Sequence Analyses

Kiyoshi Asai, Yasubumi Sakakibara, Kengo Sato, 2007, BIRD.

Stem Kernels for RNA Sequence Analyses

Kiyoshi Asai, Kris Popendorf, Yasubumi Sakakibara, 2007, BIRD.

High-frame-rate LED display with pulse-width modulation by use of nonlinear clock

Hirotsugu Yamamoto, Shiro Suyama, Kengo Sato, 2013, 2013 IEEE 2nd Global Conference on Consumer Electronics (GCCE).

Extracting Word Sequence Correspondences with Support Vector Machines

Hiroaki Saito, Kengo Sato, 2002, COLING.

Rtips: fast and accurate tools for RNA 2D structure prediction using integer programming

Tatsuya Akutsu, Kiyoshi Asai, Kengo Sato, 2012, Nucleic Acids Res..

Rtools: a web server for various secondary structural analyses on single RNA sequences

Kiyoshi Asai, Tsukasa Fukunaga, Michiaki Hamada, 2016, Nucleic Acids Res..

Pair stochastic tree adjoining grammars for aligning and predicting pseudoknot RNA structures

Hiroshi Matsui, Yasubumi Sakakibara, Kengo Sato, 2004, Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference, 2004. CSB 2004..

Direct inference of base-pairing probabilities with neural networks improves RNA secondary structure prediction with pseudoknots

Yasubumi Sakakibara, Manato Akiyama, Kengo Sato, 2018, bioRxiv.

An extended genovo metagenomic assembler by incorporating paired-end information

Afiahayati, Yasubumi Sakakibara, Kengo Sato, 2013, PeerJ.