Takashi Ishida

3.5k citations

70 papers · 2.2k indexed · 2 hit papers · h-index 17

Molecular Biology top 5%
Materials Chemistry
Cell Biology top 10%
Computational Theory and Mathematics top 5%
Plant Science top 10%

Co-authors: Kengo Kinoshita Yutaka Akiyama Shuji Suzuki Vladimir N. Uversky Julian Gough Matt E. Oates Mohamed Ghalwash Lukasz Kurgan
Topics: Protein Structure and Dynamics (28 papers)Genomics and Phylogenetic Studies (17 papers)Machine Learning in Bioinformatics (13 papers)
Cited by: Molecular Biology Computational Theory and Mathematics Cell Biology
Journals: Nucleic Acids ResearchSHILAP Revista de lepidopterologíaBioinformatics
Partner nations: Japan Australia Hungary

In The Last Decade

Takashi Ishida

66 papers receiving 2.2k 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.

PrDOS: prediction of disordered protein regions from amino acid sequence

2007 650 citations Takashi Ishida, Kengo Kinoshita Nucleic Acids Research profile →
D2P2: database of disordered protein predictions

2012 524 citations Matt E. Oates, Pedro Romero et al. Nucleic Acids Research profile →

Peers

Countries citing papers authored by Takashi Ishida

Since Specialization

Citations

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

Fields of papers citing papers by Takashi Ishida

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Takashi Ishida

This figure shows the co-authorship network connecting the top 25 collaborators of Takashi Ishida. A scholar is included among the top collaborators of Takashi Ishida 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 Takashi Ishida. Takashi Ishida 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	Discovery of a Hidden Trypanosoma cruzi Spermidine Synthase Binding Site and Inhibitors through In Silico, In Vitro , and X-ray Crystallography 2023 ·ACS Omega ·Ryunosuke Yoshino,Nobuaki Yasuo,(unknown),Takashi Ishida,D.K. Inaoka,Y. Amano,(unknown),Kazuki Ohno,Ichiji Namatame,Tatsuya Niimi,Masaya Orita,Kiyoshi Kita,Yutaka Akiyama,Masakazu Sekijima	10
2	Helix encoder: a compound-protein interaction prediction model specifically designed for class A GPCRs 2023 ·SHILAP Revista de lepidopterología ·H. Yamane,Takashi Ishida	2
3	Taxonomic and Gene Category Analyses of Subgingival Plaques from a Group of Japanese Individuals with and without Periodontitis 2021 ·International Journal of Molecular Sciences ·(unknown),(unknown),Daichi Kita,(unknown),Atsushi Saitô,Takashi Ishida,Masahito Ohue,Yutaka Akiyama,Kazuyuki Ishihara	7
4	Single-Step Retrosynthesis Prediction Based on the Identification of Potential Disconnection Sites Using Molecular Substructure Fingerprints 2021 ·Journal of Chemical Information and Modeling ·(unknown),Takashi Ishida	16
5	End-to-end learning for compound activity prediction based on binding pocket information 2021 ·BMC Bioinformatics ·(unknown),Takashi Ishida	3
6	Sequence alignment using machine learning for accurate template-based protein structure prediction 2019 ·Bioinformatics ·(unknown),Takashi Ishida	16
7	Development History and Future Perspective of a Multi-Scale Integrated Analytical System for RC Structures 2018 ·Concrete Journal ·Takashi Ishida	0
8	In Silico Prediction of Major Clearance Pathways of Drugs among 9 Routes with Two-Step Support Vector Machines 2018 ·Pharmaceutical Research ·(unknown),Kota Toshimoto,Kazuya Maeda,(unknown),Takashi Ishida,Yutaka Akiyama,Yuichi Sugiyama	11
9	Learning from Complementary Labels 2017 ·Neural Information Processing Systems ·Takashi Ishida,Gang Niu,Weihua Hu,Masashi Sugiyama	15
10	A judgment method of difficulty of task for a learner using simple electroencephalograph 2016 ·(unknown),Takashi Ishida,(unknown),Makoto Nakazawa,(unknown)	4
11	GPU-Acceleration of Sequence Homology Searches with Database Subsequence Clustering 2016 ·PLoS ONE ·Shuji Suzuki,(unknown),Takashi Ishida,Yutaka Akiyama	5
12	Development of a Support Vector Machine-Based System to Predict Whether a Compound Is a Substrate of a Given Drug Transporter Using Its Chemical Structure 2016 ·Journal of Pharmaceutical Sciences ·Atsushi Ose,Kota Toshimoto,Kazushi Ikeda,Kazuya Maeda,(unknown),Fumiyoshi Yamashita,Mitsuru Hashida,Takashi Ishida,Yutaka Akiyama,Yuichi Sugiyama	12
13	An Optimal Weighting Method in Supervised Learning of Linguistic Model for Text Classification 2012 ·Industrial Engineering & Management Systems ·(unknown),Takashi Ishida,Masayuki Goto	3
14	D2P2: database of disordered protein predictionsbreakdown → 2012 ·Nucleic Acids Research ·Matt E. Oates,Pedro Romero,Takashi Ishida,Mohamed Ghalwash,Marcin J. Mizianty,Bin Xue,Zsuzsanna Dosztányi,Vladimir N. Uversky,Zoran Obradović,Lukasz Kurgan,A. Keith Dunker,Julian Gough	524
15	GHOSTM: A GPU-Accelerated Homology Search Tool for Metagenomics 2012 ·PLoS ONE ·Shuji Suzuki,Takashi Ishida,Ken Kurokawa,Yutaka Akiyama	18
16	Absolute quality evaluation of protein model structures using statistical potentials with respect to the native and reference states 2011 ·Proteins Structure Function and Bioinformatics ·Matsuyuki Shirota,Takashi Ishida,Kengo Kinoshita	1
17	Analyses on hydrophobicity and attractiveness of all‐atom distance‐dependent potentials 2009 ·Protein Science ·Matsuyuki Shirota,Takashi Ishida,Kengo Kinoshita	3
18	PiSite: a database of protein interaction sites using multiple binding states in the PDB 2008 ·Nucleic Acids Research ·(unknown),Takashi Ishida,K. Kinoshita	42
19	Identification of transient hub proteins and the possible structural basis for their multiple interactions 2007 ·Protein Science ·(unknown),Takashi Ishida,Kengo Kinoshita	60
20	Potential for assessing quality of protein structure based on contact number prediction 2006 ·Proteins Structure Function and Bioinformatics ·Takashi Ishida,Shugo Nakamura,Kentaro Shimizu	11

About Takashi Ishida

Takashi Ishida is a scholar working on Computational Theory and Mathematics, Molecular Biology and Artificial Intelligence, having authored 70 papers that have together received 2.2k indexed citations. Recurring topics across this work include Protein Structure and Dynamics (28 papers), Genomics and Phylogenetic Studies (17 papers) and Machine Learning in Bioinformatics (13 papers). The work is most often cited by research in Molecular Biology (1.7k citations), Computational Theory and Mathematics (182 citations) and Cell Biology (187 citations). Takashi Ishida has collaborated with scholars based in Japan, Australia and Hungary. Frequent co-authors include Kengo Kinoshita, Yutaka Akiyama, Shuji Suzuki, Vladimir N. Uversky, Julian Gough, Matt E. Oates, Mohamed Ghalwash, Lukasz Kurgan, Zoran Obradović and Pedro Romero. Their work appears in journals such as Nucleic Acids Research, SHILAP Revista de lepidopterología and Bioinformatics.

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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