Kengo Sato

4.7k total citations · 2 hit papers
64 papers, 2.7k citations indexed

About

Kengo Sato is a scholar working on Molecular Biology, Artificial Intelligence and Cancer Research. According to data from OpenAlex, Kengo Sato has authored 64 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 50 papers in Molecular Biology, 9 papers in Artificial Intelligence and 5 papers in Cancer Research. Recurrent topics in Kengo Sato's work include RNA and protein synthesis mechanisms (39 papers), Genomics and Phylogenetic Studies (24 papers) and RNA modifications and cancer (24 papers). Kengo Sato is often cited by papers focused on RNA and protein synthesis mechanisms (39 papers), Genomics and Phylogenetic Studies (24 papers) and RNA modifications and cancer (24 papers). Kengo Sato collaborates with scholars based in Japan, United States and Poland. Kengo Sato's co-authors include Yasubumi Sakakibara, Michiaki Hamada, Kiyoshi Asai, Martin C. Frith, Paul Horton, Szymon M. Kiełbasa, Raymond Wan, Toutai Mituyama, Manato Akiyama and Yuki Kato and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and Bioinformatics.

In The Last Decade

Kengo Sato

60 papers receiving 2.7k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Adaptive seeds tame genomic sequence comparison

2011 853 citations Szymon M. Kiełbasa, Raymond Wan et al. Genome Research profile →
RNA secondary structure prediction using deep learning with thermodynamic integration

2021 219 citations Kengo Sato, Manato Akiyama et al. Nature Communications profile →

Peers

Countries citing papers authored by Kengo Sato

Since Specialization

Citations

This map shows the geographic impact of Kengo Sato's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Kengo Sato with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kengo Sato more than expected).

Fields of papers citing papers by Kengo Sato

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kengo Sato. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Kengo Sato. The network helps show where Kengo Sato may publish in the future.

Co-authorship network of co-authors of Kengo Sato

This figure shows the co-authorship network connecting the top 25 collaborators of Kengo Sato. A scholar is included among the top collaborators of Kengo Sato based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Kengo Sato. Kengo Sato is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	RNA secondary structure prediction using deep learning with thermodynamic integration breakdown → 2021 ·Nature Communications ·Kengo Sato, Manato Akiyama, Yasubumi Sakakibara	219
2	An improved de novo genome assembly of the common marmoset genome yields improved contiguity and increased mapping rates of sequence data 2020 ·BMC Genomics ·Vasanthan Jayakumar, (unknown), (unknown), Wakako Kumita, Takashi Inoue, (unknown), Kengo Sato, Hideyuki Okano, Erika Sasaki, Yasubumi Sakakibara	6
3	Efficient generation of Knock-in/Knock-out marmoset embryo via CRISPR/Cas9 gene editing 2019 ·Scientific Reports ·Wakako Kumita, Kenya Sato, Yasuhiro Suzuki, Yoko Kurotaki, Takeshi Harada, Yang Zhou, Noriyuki Kishi, Kengo Sato, Atsu Aiba, Yasubumi Sakakibara, Guoping Feng, Hideyuki Okano, Erika Sasaki	47
4	A max-margin training of RNA secondary structure prediction integrated with the thermodynamic model 2018 ·Journal of Bioinformatics and Computational Biology ·Manato Akiyama, Kengo Sato, Yasubumi Sakakibara	29
5	Convolutional neural network based on SMILES representation of compounds for detecting chemical motif 2018 ·BMC Bioinformatics ·(unknown), Yutaka Saitō, Yuki Koda, Kengo Sato, Yasubumi Sakakibara	141
6	DEclust: A statistical approach for obtaining differential expression profiles of multiple conditions 2017 ·PLoS ONE ·(unknown), Tsuyoshi Hachiya, Kazuhiro Okumura, (unknown), Kengo Sato, Yuichi Wakabayashi, Yasubumi Sakakibara	6
7	SHARAKU: an algorithm for aligning and clustering read mapping profiles of deep sequencing in non-coding RNA processing 2016 ·Bioinformatics ·(unknown), (unknown), (unknown), (unknown), (unknown), Kengo Sato, Yasubumi Sakakibara	3
8	Analysis of Local Fracture Strain and Damage Limit ofAdvanced High Strength Steels using MeasuredDisplacement Fields and FEM 2015 ·Cmc-computers Materials & Continua ·Ninshu Ma, Kengo Sato, Kenji Takada	5
9	Resequencing of the common marmoset genome improves genome assemblies and gene-coding sequence analysis 2015 ·Scientific Reports ·Kengo Sato, Yoko Kuroki, Wakako Kumita, Asao Fujiyama, Atsushi Toyoda, Jun Kawai, Atsushi Iriki, Erika Sasaki, Hideyuki Okano, Yasubumi Sakakibara	26
10	Whole-Genome Sequencing and Comparative Genome Analysis of Bacillus subtilis Strains Isolated from Non-Salted Fermented Soybean Foods 2015 ·PLoS ONE ·Mayumi Kamada, (unknown), (unknown), Masato Miyake, Kengo Sato, Keitarou Kimura, Yasubumi Sakakibara	36
11	Whole Genome Complete Resequencing of Bacillus subtilis Natto by Combining Long Reads with High-Quality Short Reads 2014 ·PLoS ONE ·Mayumi Kamada, (unknown), Kengo Sato, Atsushi Toyoda, Asao Fujiyama, Yasubumi Sakakibara	38
12	An efficient algorithm for de novo predictions of biochemical pathways between chemical compounds 2012 ·BMC Bioinformatics ·(unknown), Tsuyoshi Hachiya, Yutaka Saitō, Kengo Sato, Yasubumi Sakakibara	19
13	Rtips: fast and accurate tools for RNA 2D structure prediction using integer programming 2012 ·Nucleic Acids Research ·Yuki Kato, Kengo Sato, Kiyoshi Asai, Tatsuya Akutsu	24
14	Adaptive seeds tame genomic sequence comparison breakdown → 2011 ·Genome Research ·Szymon M. Kiełbasa, Raymond Wan, Kengo Sato, Paul Horton, Martin C. Frith	853
15	CentroidHomfold-LAST: accurate prediction of RNA secondary structure using automatically collected homologous sequences 2011 ·Nucleic Acids Research ·Michiaki Hamada, Koichiro Yamada, Kengo Sato, Martin C. Frith, Kiyoshi Asai	19
16	Robust and accurate prediction of noncoding RNAs from aligned sequences 2010 ·BMC Bioinformatics ·Yutaka Saitō, Kengo Sato, Yasubumi Sakakibara	1
17	IMPROVEMENT OF STRUCTURE CONSERVATION INDEX WITH CENTROID ESTIMATORS 2009 ·WORLD SCIENTIFIC eBooks ·Yohei Okada, Kengo Sato, Yasubumi Sakakibara	3
18	Grammatical Inference: Algorithms and Applications: 8th International Colloquium, ICGI 2006, Tokyo, Japan, September 20-22, 2006, Proceedings (Lecture Notes in Computer Science) 2006 ·Springer eBooks ·(unknown), Satoshi Kobayashi, Kengo Sato, (unknown), Etsuji Tomita	1
19	Pair stochastic tree adjoining grammars for aligning and predicting pseudoknot RNA structures 2005 ·Computer applications in the biosciences ·(unknown), Kengo Sato, Yasubumi Sakakibara	43
20	Extracting Word Sequence Correspondences Based on Support Vector Machines. 2003 ·Journal of Natural Language Processing ·Kengo Sato, Hiroaki Saito	0

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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