Demo Data from EDBT 2026 / reviewers in the wild / expert
Xianjie Huang
dblp:309/3842
· DBLP profile ↗
2ranked-venue papers
2as first author
2since 2021 · last 2026
—ORCID · conflict
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 · 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.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
genomics |
0.5 | 1 | 2021 | Cellsnp-lite: an efficient tool for genotyping single cells · Bioinform. 2021 |
Bioinformatics and computational biology › genomics
genotyping |
0.5 | 1 | 2021 | Cellsnp-lite: an efficient tool for genotyping single cells · Bioinform. 2021 |
Bioinformatics and computational biology › single-cell analysis
single-cell sequencing |
0.5 | 1 | 2021 | Cellsnp-lite: an efficient tool for genotyping single cells · Bioinform. 2021 |
Bioinformatics and computational biology › genomics
variant calling |
0.1 | 1 | 2021 | Cellsnp-lite: an efficient tool for genotyping single cells · Bioinform. 2021 |
Methods — techniques the papers use, named apart from their topics
HTSlib · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | SDGraphMeta: A novel similarity-driven framework for graph meta-learning
Xianjie Huang, Jian Li 0032, Guanjun Liu |
Pattern Recognit. Lett. | 1 |
| 2021 | Cellsnp-lite: an efficient tool for genotyping single cellsabstractSUMMARY: Single-cell sequencing is an increasingly used technology and has promising applications in basic research and clinical translations. However, genotyping methods developed for bulk sequencing data have not been well adapted for single-cell data, in terms of both computational parallelization and simplified user interface. Here, we introduce a software, cellsnp-lite, implemented in C/C++ and based on well-supported package htslib, for genotyping in single-cell sequencing data for both droplet and well-based platforms. On various experimental datasets, it shows substantial improvement in computational speed and memory efficiency with retaining highly concordant results compared to existing methods. Cellsnp-lite, therefore, lightens the genetic analysis for increasingly large single-cell data. AVAILABILITY AND IMPLEMENTATION: The source code is freely available at https://github.com/single-cell-genetics/cellsnp-lite. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Xianjie Huang, Yuanhua Huang |
Bioinform. | 1 |