Demo Data from EDBT 2026 / reviewers in the wild / expert
Chunxia Qin
dblp:209/8090
· DBLP profile ↗
2ranked-venue papers
1as first author
1since 2021 · last 2024
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 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.
| Artificial intelligence
1 paper |
Information extraction and text analysis · 67% Image recognition and object detection · 33% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Data integration and cleaning · 100% |
Topics — the 8 heaviest of 8, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Information extraction and text analysis › document understanding
document layout analysis |
0.8 | 1 | 2024 | SEMv3: A Fast and Robust Approach to Table Separation Line Detection · IJCAI 2024 |
Computer vision › Image recognition and object detection › visual feature analysis
line detection |
0.8 | 1 | 2024 | SEMv3: A Fast and Robust Approach to Table Separation Line Detection · IJCAI 2024 |
Natural language and speech › Information extraction and text analysis › document understanding › table recognition
table structure recognition |
0.8 | 1 | 2024 | SEMv3: A Fast and Robust Approach to Table Separation Line Detection · IJCAI 2024 |
Bioinformatics and computational biology › epigenomics › differential methylation analysis
differentially methylated region detection |
0.3 | 1 | 2017 | Genome-wide analysis of DNA methylation profiles in a senescence-accelerated mouse prone 8 brain using whole-genome bisulfite sequencing · Bioinform. 2017 |
Bioinformatics and computational biology › epigenomics › DNA methylation
DNA methylation profiling |
0.3 | 1 | 2017 | Genome-wide analysis of DNA methylation profiles in a senescence-accelerated mouse prone 8 brain using whole-genome bisulfite sequencing · Bioinform. 2017 |
Bioinformatics and computational biology
epigenomics |
0.3 | 1 | 2017 | Genome-wide analysis of DNA methylation profiles in a senescence-accelerated mouse prone 8 brain using whole-genome bisulfite sequencing · Bioinform. 2017 |
Data integration and cleaning › data extraction
table extraction |
0.2 | 1 | 2024 | SEMv3: A Fast and Robust Approach to Table Separation Line Detection · IJCAI 2024 |
Bioinformatics and computational biology
alzheimer's disease |
0.1 | 1 | 2017 | Genome-wide analysis of DNA methylation profiles in a senescence-accelerated mouse prone 8 brain using whole-genome bisulfite sequencing · Bioinform. 2017 |
Methods — techniques the papers use, named apart from their topics
table separation line detection · 1.5whole-genome bisulfite sequencing · 0.3RNA sequencing · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | SEMv3: A Fast and Robust Approach to Table Separation Line Detection
Chunxia Qin, Pengfei Hu 0006, Jiefeng Ma, Jun Du 0002 |
IJCAI | 1 |
| 2017 | Genome-wide analysis of DNA methylation profiles in a senescence-accelerated mouse prone 8 brain using whole-genome bisulfite sequencingabstractMOTIVATION: The pathogenesis of AD is complex and contributed by both genetic and environmental factors. Recent work revealed a potential link between DNA methylation and AD. However, a genome-wide study to identify potential DNA methylation sites involved in AD is still at an early stage. WGBS, an up-to-date technology, was used in this study. We investigated mouse brain genome-wide DNA methylation profiles between seven-month-old SAMP8 and SAMR1 models through deep WGBS. RESULTS: According to the results, the global ML slightly decreased in the SAMP8 mice than in the SAMR1 mice (4.12% versus 4.19%). A total of 1 307 172 280 clean reads were obtained. Subsequently, we identified 63 DMRs from all cases in SAMP8 mice relative to SAMR1 mice. In addition, 26 DMR-related genes were detected. GO analyses revealed that these DMR-related genes were involved in regulating the development of AD from different aspects. Finally, three differentially expressed DMR-related genes ( Dlgap1 , TMEM51 and Eif2ak2 ) that were most likely involved in AD were summarized and listed in detail. Our study provided a systematic exploration of DNA methylation profiles in SAMP8 mouse brain for the first time. These novel methylation sites may be considered strong future candidates to combat this life-threatening disease. AVAILABILITY AND IMPLEMENTATION: The WGBS sequencing clean data and RNA-seq clean data have been deposited in the NCBI Sequence Read Archive (SRA).The accession number of WGBS is SRP097054. The accession number of RNA-seq is SRP096779. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Chunxia Qin, Guoqiong Cao, Chengqiang Feng |
Bioinform. | 2 |