Demo Data from SIGIR 2026 / reviewers in the wild / expert
Tony Kuo
dblp:52/2404
· DBLP profile ↗
2ranked-venue papers
0as first author
1since 2021 · last 2025
0000-0003-2358-7919ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021
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 · 87% Medical and health informatics · 13% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
single-cell analysis |
0.9 | 1 | 2025 | Learning Multi-cellular Representations of Single-Cell Transcriptomics Data Enables Characterization of Patient-Level Disease States · RECOMB 2025 |
Bioinformatics and computational biology › single-cell analysis
single-cell representation learning |
0.9 | 1 | 2025 | Learning Multi-cellular Representations of Single-Cell Transcriptomics Data Enables Characterization of Patient-Level Disease States · RECOMB 2025 |
Methods — techniques the papers use, named apart from their topics
machine learning · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Learning Multi-cellular Representations of Single-Cell Transcriptomics Data Enables Characterization of Patient-Level Disease States
Tianyu Liu 0005, Edward De Brouwer, Tony Kuo, Nathaniel Diamant, Alsu Missarova, Hanchen Wang 0002, Minsheng Hao, Héctor Corrada Bravo, Gabriele Scalia, Aviv Regev, Graham Heimberg |
RECOMB | 3 |
| 2006 | Visio-Spatial Case-Based Reasoning: A Case Study in Prediction of Protein StructureabstractWe show that visio‐spatial representations and reasoning can be used as a similarity metric for case‐based protein structure prediction. Our system retrieves pairs of α‐helices based on contact map similarity, then transfers and adapts the structure information to an unknown helix pair. We show that similar protein contact maps predict a similar three‐dimensional protein structure. The success of this method provides support for the notion that changing representations can enable similarity metrics in case‐based reasoning. Jim Davies, Janice I. Glasgow, Tony Kuo |
Comput. Intell. | 3 |