Demo Data from EDBT 2026 / reviewers in the wild / expert
Jingxin Yan
dblp:352/9849
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2025
0000-0002-2734-6146ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 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.
| Theoretical computer science
1 paper |
Mathematical optimization · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 2 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › statistical genetics › genetic association study
genetic association testing |
0.9 | 1 | 2025 | The sequence kernel association test for the proportional odds model · Bioinform. 2025 |
Mathematical optimization
rank correlation |
0.7 | 1 | 2023 | Spearman Rank Correlation Screening for Ultrahigh-Dimensional Censored Data · AAAI 2023 |
Methods — techniques the papers use, named apart from their topics
score test · 0.9quasi-likelihood · 0.9Pearson Type III approximation · 0.9sure screening · 0.7spearman rank correlation · 0.7rank consistency · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | The sequence kernel association test for the proportional odds modelabstractMOTIVATION: The Sequence Kernel Association Test (SKAT) and its extensions are the most popular methods for studying the association between phenotypes and a set of single nucleotide polymorphisms. Their practical application is very wide, but most of these methods are designed for continuous and binary phenotypes. Ordered categorical phenotypes are also very common in practice, so there is an urgent need to develop SKAT-type tests for proportional odds model. RESULTS: To accommodate ordered categorical phenotypes, we propose a test named the Sequence Kernel Association Test for the Proportional Odds Model (POM-SKAT). It constructs a score test for the variance of the coefficients of interest using a quasi-likelihood and the P-value is evaluated by approximating the asymptotic distribution of the test statistic with the Pearson Type III distribution. Simulation studies demonstrate that our method performs well and achieves high power in detecting gene-phenotype associations. We apply POM-SKAT to rheumatoid arthritis data provided by Genetic Analysis Workshop 16, identifying multiple relevant gene variants. AVAILABILITY AND IMPLEMENTATION: Code is available at GitHub (https://github.com/amss-stat/POM-SKAT). Jingxin Yan, Shuying Wang, Jinjuan Wang, Qizhai Li |
Bioinform. | 1 |
| 2023 | Spearman Rank Correlation Screening for Ultrahigh-Dimensional Censored DataabstractHerein, we propose a Spearman rank correlation-based screening procedure for ultrahigh-dimensional data with censored response cases. The proposed method is model-free without specifying any regression forms of predictors or response variables and is robust under the unknown monotone transformations of these response variable and predictors. The sure-screening and rank-consistency properties are established under some mild regularity conditions. Simulation studies demonstrate that the new screening method performs well in the presence of a heavy-tailed distribution, strongly dependent predictors or outliers, and offers superior performance over the existing nonparametric screening procedures. In particular, the new screening method still works well when a response variable is observed under a high censoring rate. An illustrative example is provided. Hongni Wang, Jingxin Yan |
AAAI | 2 |