Demo Data from VLDB 2026 / reviewers in the wild / expert
Po-Ju Yao
dblp:176/9811
· DBLP profile ↗
2ranked-venue papers
1as first author
0since 2021 · last 2016
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 2 · 1 first-author
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 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › statistical genetics
genetic data simulation |
0.2 | 1 | 2016 | SeqSIMLA2_exact: simulate multiple disease sites in large pedigrees with given disease status for diseases with low prevalence · Bioinform. 2016 |
Bioinformatics and computational biology
disease genetics |
0.1 | 1 | 2016 | SeqSIMLA2_exact: simulate multiple disease sites in large pedigrees with given disease status for diseases with low prevalence · Bioinform. 2016 |
Methods — techniques the papers use, named apart from their topics
coalescent simulation · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | SeqSIMLA2_exact: simulate multiple disease sites in large pedigrees with given disease status for diseases with low prevalenceabstractUNLABELLED: It is difficult for current simulation tools to simulate sequence data in a pre-specified pedigree structure and pre-specified affection status. Previously, we developed a flexible tool, SeqSIMLA2, for simulating sequence data in either unrelated case-control or family samples with different disease and quantitative trait models. Here we extended the tool to efficiently simulate sequences with multiple disease sites in large pedigrees with a given disease status for each pedigree member, assuming that the disease prevalence is low. AVAILABILITY AND IMPLEMENTATION: SeqSIMLA2_exact is implemented with C++ and is available at http://seqsimla.sourceforge.net. Po-Ju Yao, Ren-Hua Chung |
Bioinform. | 1 |
| 2016 | FamPipe: An Automatic Analysis Pipeline for Analyzing Sequencing Data in Families for Disease StudiesabstractIn disease studies, family-based designs have become an attractive approach to analyzing next-generation sequencing (NGS) data for the identification of rare mutations enriched in families. Substantial research effort has been devoted to developing pipelines for automating sequence alignment, variant calling, and annotation. However, fewer pipelines have been designed specifically for disease studies. Most of the current analysis pipelines for family-based disease studies using NGS data focus on a specific function, such as identifying variants with Mendelian inheritance or identifying shared chromosomal regions among affected family members. Consequently, some other useful family-based analysis tools, such as imputation, linkage, and association tools, have yet to be integrated and automated. We developed FamPipe, a comprehensive analysis pipeline, which includes several family-specific analysis modules, including the identification of shared chromosomal regions among affected family members, prioritizing variants assuming a disease model, imputation of untyped variants, and linkage and association tests. We used simulation studies to compare properties of some modules implemented in FamPipe, and based on the results, we provided suggestions for the selection of modules to achieve an optimal analysis strategy. The pipeline is under the GNU GPL License and can be downloaded for free at http://fampipe.sourceforge.net. Ren-Hua Chung, Wei-Yun Tsai, Chen-Yu Kang, Po-Ju Yao, Hui-Ju Tsai |
PLoS Comput. Biol. | 4 |