Demo Data from EDBT 2026 / reviewers in the wild / expert
Mitchell H. Gail
dblp:12/8019
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2018
0000-0002-3919-3263ORCID · 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 › computational microbiology › microbiome analysis
beta-diversity analysis |
0.3 | 1 | 2018 | Using standard microbiome reference groups to simplify beta-diversity analyses and facilitate independent validation · Bioinform. 2018 |
Bioinformatics and computational biology › computational microbiology
microbiome analysis |
0.3 | 1 | 2018 | Using standard microbiome reference groups to simplify beta-diversity analyses and facilitate independent validation · Bioinform. 2018 |
Bioinformatics and computational biology › biostatistics › statistical bioinformatics
statistical genomics |
0.3 | 1 | 2018 | Using standard microbiome reference groups to simplify beta-diversity analyses and facilitate independent validation · Bioinform. 2018 |
Methods — techniques the papers use, named apart from their topics
pearson correlation · 0.3hotelling test · 0.3bray-curtis dissimilarity · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | Using standard microbiome reference groups to simplify beta-diversity analyses and facilitate independent validationabstractMotivation: Comparisons of microbiome communities across populations are often based on pairwise distance measures (beta-diversity). Standard analyses (principal coordinate plots, permutation tests, kernel methods) require access to primary data if another investigator wants to add or compare independent data. We propose using standard reference measurements to simplify microbiome beta-diversity analyses, to make them more transparent, and to facilitate independent validation and comparisons across studies. Results: Using stool and nasal reference sets from the Human Microbiome Project (HMP), we computed mean distances (actually Bray-Curtis or Pearson correlation dissimilarities) to each reference set for each new sample. Thus, each new sample has two mean distances that can be plotted and analyzed with classical statistical methods. To test the approach, we studied independent (not reference) HMP subjects. Simple Hotelling tests demonstrated statistically significant differences in mean distances to reference sets between all pairs of body sites (stool, skin, nasal, saliva and vagina) at the phylum, class, order, family and genus levels. Using the distance to a single reference set was usually sufficient, but using both reference sets always worked well. The use of reference sets simplifies standard analyses of beta-diversity and facilitates the independent validation and combining of such data because others can compute distances to the same reference sets. Moreover, standard statistical methods for survival analysis, logistic regression and other procedures can be applied to vectors of mean distances to reference sets, thereby greatly expanding the potential uses of beta-diversity information. More work is needed to identify the best reference sets for particular applications. Availability and implementation: https://github.com/NCI-biostats/microbiome-fixed-reference. Supplementary information: Supplementary data are available at Bioinformatics online. Marlena Maziarz, Ruth M. Pfeiffer, Yunhu Wan, Mitchell H. Gail |
Bioinform. | 4 |