Demo Data from EDBT 2026 / reviewers in the wild / expert
Scott Leishman
dblp:91/4619
· DBLP profile ↗
3ranked-venue papers
1as first author
0since 2021 · last 2005
0000-0001-6681-2384ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Software engineering, systems software and programming languages · 1Databases, data management, data science and information retrieval · 1Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author
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.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › structural bioinformatics › protein structure determination
NMR resonance assignment |
0.0 | 1 | 1995 | A Constraint-Based Assignment System for Automating Long Side Chain Assignments in Protein 2D NMR Spectra · ISMB 1995 |
Bioinformatics and computational biology › structural bioinformatics
protein structure determination |
0.0 | 1 | 1995 | A Constraint-Based Assignment System for Automating Long Side Chain Assignments in Protein 2D NMR Spectra · ISMB 1995 |
Bioinformatics and computational biology › protein structure analysis
side-chain resonance assignment |
0.0 | 1 | 1995 | A Constraint-Based Assignment System for Automating Long Side Chain Assignments in Protein 2D NMR Spectra · ISMB 1995 |
Methods — techniques the papers use, named apart from their topics
constraint-based assignment · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2005 | A Statistical Learning Approach To Document Image AnalysisabstractIn the field of computer analysis of document images, the problems of physical and logical layout analysis have been approached through a variety of heuristic, rule-based, and grammar-based techniques. In this paper we investigate the effectiveness of statistical pattern recognition algorithms for solving these two problems, and report results suggesting that these more complex and powerful techniques are worth pursuing. First, we developed a new software environment for manual page image segmentation and labeling, and used it to create a dataset containing 932 page images from academic journals. Next, a physical layout analysis algorithm based on a logistic regression classifier was developed, and found to outperform existing algorithms of comparable complexity. Finally, three statistical classifiers were applied to the logical layout analysis problem, also with encouraging results. Kevin Laven, Scott Leishman, Sam T. Roweis |
ICDAR | 2 |
| 1995 | A Constraint-Based Assignment System for Automating Long Side Chain Assignments in Protein 2D NMR Spectra
Scott Leishman, Peter M. D. Gray, John E. Fothergill |
ISMB | 1 |
| 1993 | On using Prolog to implement object-oriented databases
Norman W. Paton, Scott Leishman, Suzanne M. Embury, Peter M. D. Gray |
Inf. Softw. Technol. | 2 |