Demo Data from SIGIR 2026 / reviewers in the wild / expert
Yunxiang Mu
dblp:77/2874
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2006
0000-0001-9754-2129ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Applied, 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 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › gene expression analysis
differential expression analysis |
0.1 | 1 | 2006 | Identification of humoral immune responses in protein microarrays using DNA microarray data analysis techniques · Bioinform. 2006 |
Bioinformatics and computational biology
immunoinformatics |
0.1 | 1 | 2006 | Identification of humoral immune responses in protein microarrays using DNA microarray data analysis techniques · Bioinform. 2006 |
Bioinformatics and computational biology › gene expression analysis
microarray data analysis |
0.1 | 1 | 2006 | Identification of humoral immune responses in protein microarrays using DNA microarray data analysis techniques · Bioinform. 2006 |
Methods — techniques the papers use, named apart from their topics
variance estimation · 0.1normalization · 0.1differential expression analysis · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2006 | Identification of humoral immune responses in protein microarrays using DNA microarray data analysis techniquesabstractMOTIVATION: We present a study of antigen expression signals from a newly developed high-throughput protein microarray technique. These signals are a measure of antibody-antigen binding activity and provide a basis for understanding humoral immune responses to various infectious agents and supporting vaccine and diagnostic development. RESULTS: We investigate the characteristics of these expression profiles and show that noise models, normalization, variance estimation and differential expression analysis techniques developed in the context of DNA microarray analysis can be adapted and applied to these protein arrays. Using a high-dimensional dataset containing measurements of expression profiles of antibody reactivity against each protein (295 antigens and 9 controls) in 42 malaria (Plasmodium falciparum) protein arrays derived from 22 donors with various clinical presentations of malaria, we present a methodology for the analysis and identification of significantly expressed antigens targeted by immune responses for individual sera, groups of sera and across stages of infection. We also conduct a short study highlighting the top immunoreactive antigens where we identify three novel high priority antigens for future evaluation. AVAILABILITY: All software programs (in R) used for the analysis described in this paper are freely available for academic purposes at www.igb.uci.edu/servers/servers.html. Suman Sundaresh, Denise L. Doolan, Siddiqua Hirst, Yunxiang Mu, Berkay Unal, D. Huw Davies, Philip L. Felgner, Pierre Baldi |
Bioinform. | 4 |