Demo Data from EDBT 2026 / reviewers in the wild / expert
Qian Zhai
dblp:26/9536
· DBLP profile ↗
2ranked-venue papers
1as first author
1since 2021 · last 2026
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Applied, 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.
| 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 › RNA biology › RNA analysis › RNA bioinformatics
microRNA prediction |
0.1 | 1 | 2011 | Prediction of novel pre-microRNAs with high accuracy through boosting and SVM · Bioinform. 2011 |
Bioinformatics and computational biology › transcriptomics
non-coding RNA analysis |
0.1 | 1 | 2011 | Prediction of novel pre-microRNAs with high accuracy through boosting and SVM · Bioinform. 2011 |
Bioinformatics and computational biology › transcriptomics › non-coding RNA analysis
pre-miRNA prediction |
0.1 | 1 | 2011 | Prediction of novel pre-microRNAs with high accuracy through boosting and SVM · Bioinform. 2011 |
Methods — techniques the papers use, named apart from their topics
support vector machine · 0.1feature selection · 0.1boosting · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Design of a low-power constraint communication AI fusion model compression algorithm
Qian Zhai |
Neural Comput. Appl. | 1 |
| 2011 | Prediction of novel pre-microRNAs with high accuracy through boosting and SVMabstractUNLABELLED: High-throughput deep-sequencing technology has generated an unprecedented number of expressed short sequence reads, presenting not only an opportunity but also a challenge for prediction of novel microRNAs. To verify the existence of candidate microRNAs, we have to show that these short sequences can be processed from candidate pre-microRNAs. However, it is laborious and time consuming to verify these using existing experimental techniques. Therefore, here, we describe a new method, miRD, which is constructed using two feature selection strategies based on support vector machines (SVMs) and boosting method. It is a high-efficiency tool for novel pre-microRNA prediction with accuracy up to 94.0% among different species. AVAILABILITY: miRD is implemented in PHP/PERL+MySQL+R and can be freely accessed at http://mcg.ustc.edu.cn/rpg/mird/mird.php. Yuanwei Zhang, Yifan Yang 0001, Xiaohua Jiang, Yu Xue 0001, Yunxia Cao, Qian Zhai, Yong Zhai, Mingqing Xu, Howard J. Cooke, Qinghua Shi |
Bioinform. | 8 |