Hiroto Saigo

2.1k total citations
32 papers, 1.3k citations indexed

About

Hiroto Saigo is a scholar working on Molecular Biology, Computational Theory and Mathematics and Spectroscopy. According to data from OpenAlex, Hiroto Saigo has authored 32 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Molecular Biology, 7 papers in Computational Theory and Mathematics and 6 papers in Spectroscopy. Recurrent topics in Hiroto Saigo's work include Machine Learning in Bioinformatics (14 papers), Computational Drug Discovery Methods (6 papers) and Advanced Proteomics Techniques and Applications (5 papers). Hiroto Saigo is often cited by papers focused on Machine Learning in Bioinformatics (14 papers), Computational Drug Discovery Methods (6 papers) and Advanced Proteomics Techniques and Applications (5 papers). Hiroto Saigo collaborates with scholars based in Japan, United States and Germany. Hiroto Saigo's co-authors include Pierre Baldi, Tatsuya Akutsu, Jean‐Philippe Vert, Nobuhisa Ueda, S. Joshua Swamidass, Liva Ralaivola, Koji Tsuda, Jianlin Cheng, Dukka B. KC and Robert H. Newman and has published in prestigious journals such as Bioinformatics, Scientific Reports and BMC Bioinformatics.

In The Last Decade

Hiroto Saigo

32 papers receiving 1.3k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Hiroto Saigo Japan 17 809 338 298 150 125 32 1.3k
Liva Ralaivola France 11 550 0.7× 348 1.0× 299 1.0× 92 0.6× 130 1.0× 26 1.0k
Lee Sael South Korea 21 698 0.9× 301 0.9× 216 0.7× 140 0.9× 261 2.1× 60 1.3k
Sergio Decherchi Italy 20 853 1.1× 452 1.3× 196 0.7× 106 0.7× 217 1.7× 71 1.5k
Minghua Deng China 28 1.9k 2.3× 378 1.1× 397 1.3× 260 1.7× 65 0.5× 88 2.5k
Rohit Singh United States 15 1.1k 1.3× 356 1.1× 347 1.2× 95 0.6× 65 0.5× 43 1.5k
Laurent Jacob France 16 905 1.1× 369 1.1× 314 1.1× 166 1.1× 77 0.6× 30 1.7k
Thorsten Meinl Germany 11 487 0.6× 396 1.2× 190 0.6× 87 0.6× 74 0.6× 29 1.2k
Kilian Thiel Germany 7 338 0.4× 229 0.7× 182 0.6× 70 0.5× 48 0.4× 10 942
Gargi Debnath United States 13 332 0.4× 211 0.6× 295 1.0× 98 0.7× 63 0.5× 13 1.0k
Demi Guo United States 7 1.3k 1.5× 244 0.7× 279 0.9× 106 0.7× 124 1.0× 9 1.7k

Countries citing papers authored by Hiroto Saigo

Since Specialization
Citations

This map shows the geographic impact of Hiroto Saigo'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 Hiroto Saigo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hiroto Saigo more than expected).

Fields of papers citing papers by Hiroto Saigo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hiroto Saigo. 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 Hiroto Saigo. The network helps show where Hiroto Saigo may publish in the future.

Co-authorship network of co-authors of Hiroto Saigo

This figure shows the co-authorship network connecting the top 25 collaborators of Hiroto Saigo. A scholar is included among the top collaborators of Hiroto Saigo 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 Hiroto Saigo. Hiroto Saigo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Saigo, Hiroto, Dukka B. KC, & Noritaka Saito. (2022). Einstein–Roscoe regression for the slag viscosity prediction problem in steelmaking. Scientific Reports. 12(1). 6541–6541. 10 indexed citations
3.
Saigo, Hiroto, et al.. (2022). Deep Learning–Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction. Methods in molecular biology. 2499. 285–322. 10 indexed citations
4.
Thapa, Niraj, et al.. (2020). DeepRMethylSite: a deep learning based approach for prediction of arginine methylation sites in proteins. Molecular Omics. 16(5). 448–454. 23 indexed citations
5.
Thapa, Niraj, et al.. (2020). DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction. BMC Bioinformatics. 21(S3). 63–63. 55 indexed citations
6.
Thapa, Niraj, et al.. (2020). RF-MaloSite and DL-Malosite: Methods based on random forest and deep learning to identify malonylation sites. Computational and Structural Biotechnology Journal. 18. 852–860. 18 indexed citations
7.
Saigo, Hiroto, et al.. (2019). RF-GlutarySite: a random forest based predictor for glutarylation sites. Molecular Omics. 15(3). 189–204. 34 indexed citations
8.
Ismail, Hamid D., et al.. (2017). CNN-BLPred: a Convolutional neural network based predictor for β-Lactamases (BL) and their classes. BMC Bioinformatics. 18(S16). 577–577. 21 indexed citations
9.
Ismail, Hamid D., Hiroto Saigo, & Dukka B. KC. (2017). RF-NR: Random Forest Based Approach for Improved Classification of Nuclear Receptors. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 15(6). 1844–1852. 11 indexed citations
10.
Saigo, Hiroto, André Altmann, Jasmina Bogojeska, et al.. (2011). Learning from Past Treatments and Their Outcome Improves Prediction of In Vivo Response to Anti-HIV Therapy. Statistical Applications in Genetics and Molecular Biology. 10(1). Article 6–Article 6. 17 indexed citations
11.
Yamanishi, Yoshihiro, Edouard Pauwels, Hiroto Saigo, & Véronique Stoven. (2011). Extracting Sets of Chemical Substructures and Protein Domains Governing Drug-Target Interactions. Journal of Chemical Information and Modeling. 51(5). 1183–1194. 56 indexed citations
12.
Saigo, Hiroto, Masahiro Hattori, Hisashi Kashima, & Koji Tsuda. (2010). Reaction graph kernels predict EC numbers of unknown enzymatic reactions in plant secondary metabolism. BMC Bioinformatics. 11(S1). S31–S31. 11 indexed citations
13.
Saigo, Hiroto & Koji Tsuda. (2008). Iterative Subgraph Mining for Principal Component Analysis. Max Planck Institute for Plasma Physics. 2002. 1007–1012. 1 indexed citations
14.
Saigo, Hiroto, Jean‐Philippe Vert, & Tatsuya Akutsu. (2006). Optimizing amino acid substitution matrices with a local alignment kernel. BMC Bioinformatics. 7(1). 246–246. 30 indexed citations
15.
Danziger, Samuel A., S. Joshua Swamidass, Jue Zeng, et al.. (2006). Functional Census of Mutation Sequence Spaces: The Example of p53 Cancer Rescue Mutants. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 3(2). 114–125. 24 indexed citations
16.
Saigo, Hiroto, et al.. (2006). A Linear Programming Approach for Molecular QSAR analysis. Max Planck Institute for Plasma Physics. 85–96. 11 indexed citations
17.
Matsuda, Setsuro, Jean‐Philippe Vert, Hiroto Saigo, et al.. (2005). A novel representation of protein sequences for prediction of subcellular location using support vector machines. Protein Science. 14(11). 2804–2813. 119 indexed citations
18.
Ralaivola, Liva, S. Joshua Swamidass, Hiroto Saigo, & Pierre Baldi. (2005). Graph kernels for chemical informatics. Neural Networks. 18(8). 1093–1110. 286 indexed citations
19.
Cheng, Jianlin, Hiroto Saigo, & Pierre Baldi. (2005). Large‐scale prediction of disulphide bridges using kernel methods, two‐dimensional recursive neural networks, and weighted graph matching. Proteins Structure Function and Bioinformatics. 62(3). 617–629. 109 indexed citations
20.
Saigo, Hiroto, Jean‐Philippe Vert, Tatsuya Akutsu, & Nobuhisa Ueda. (2002). Comparison of SVM-Based Methods for Remote Homology Detection. Proceedings Genome Informatics Workshop/Genome informatics. 13(13). 396–397. 4 indexed citations

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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