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Qian Zhai

dblp:26/9536 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › RNA biology › RNA analysis › RNA bioinformatics
microRNA prediction
0.112011
Prediction of novel pre-microRNAs with high accuracy through boosting and SVM · Bioinform. 2011
Bioinformatics and computational biology › transcriptomics
non-coding RNA analysis
0.112011
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
0.112011
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
YearPublicationVenuePosition
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 SVM
abstract
UNLABELLED: 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