Demo Data from EDBT 2026 / reviewers in the wild / expert
Makoto Ando
dblp:11/5723
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2004
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1Human-computer interaction and ubiquitous computing · 1Applied, interdisciplinary, general and emerging computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
1 paper |
Immersive interaction · 77% Learning and educational technologies · 23% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
protein structure analysis |
0.0 | 1 | 2000 | Quick selection of representative protein chain sets based on customizable requirements · Bioinform. 2000 |
Bioinformatics and computational biology › structural bioinformatics
protein structure classification |
0.0 | 1 | 2000 | Quick selection of representative protein chain sets based on customizable requirements · Bioinform. 2000 |
Learning and educational technologies › informal learning
museum learning |
0.0 | 1 | 2004 | A Case Study of Museum Exhibition -Historical Learning in Copan Ruins of Mayan Civilization · VR 2004 |
Methods — techniques the papers use, named apart from their topics
structural similarity · 0.0sequence similarity · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2004 | A Case Study of Museum Exhibition -Historical Learning in Copan Ruins of Mayan Civilization
Tomohiro Tanikawa, Makoto Ando, Yankang Wang, Kazuhiro Yoshida, Jun Yamashita, Hideaki Kuzuoka, Michitaka Hirose |
VR | 2 |
| 2000 | Quick selection of representative protein chain sets based on customizable requirementsabstractAbstract Motivation: Protein structure classification has been recognized as one of the most important research issues in protein structure analysis. A substantial number of methods for the classification have been proposed, and several databases have been constructed using these methods. Since some proteins with very similar sequences may exhibit structural diversities, we have proposed PDB-REPRDB: a database of representative protein chains from the Protein Data Bank (PDB), which strategy of selection is based not only on sequence similarity but also on structural similarity. Forty-eight representative sets whose similarity criteria were predetermined were made available over the World Wide Web (WWW). However, the sets were insufficient in number to satisfy users researching protein structures by various methods. Result: We have improved the system for PDB-REPRDB so that the user may obtain a quick selection of representative chains from PDB. The selection of representative chains can be dynamically configured according to the user’s requirement. The WWW interface provides a large degree of freedom in setting parameters, such as cut-off scores of sequence and structural similarity. This paper describes the method we use to classify chains and select the representatives in the system. We also describe the interface used to set the parameters. Availability: The system for PDB-REPRDB is available at the PAPIA WWW server (http://www.rwcp.or.jp/papia/). Contact: [email protected] * To whom correspondence should be addressed. 3 Present address: Information Processing Systems Department, NKK Corporation, 1-1-2 Marunouchi, Chiyoda-ku, Tokyo 100-8202, Japan Tamotsu Noguchi, Kentaro Onizuka, Makoto Ando, Hideo Matsuda, Yutaka Akiyama |
Bioinform. | 3 |