Demo Data from VLDB 2026 / reviewers in the wild / expert
George Mias
dblp:250/6775 · also George I. Mias
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2020
0000-0002-9083-1216ORCID · 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 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › omics data analysis
longitudinal omics analysis |
0.4 | 1 | 2020 | PyIOmica: longitudinal omics analysis and trend identification · Bioinform. 2020 |
Methods — techniques the papers use, named apart from their topics
visibility graph · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | PyIOmica: longitudinal omics analysis and trend identificationabstractSUMMARY: PyIOmica is an open-source Python package focusing on integrating longitudinal multiple omics datasets, characterizing and categorizing temporal trends. The package includes multiple bioinformatics tools including data normalization, annotation, categorization, visualization and enrichment analysis for gene ontology terms and pathways. Additionally, the package includes an implementation of visibility graphs to visualize time series as networks. AVAILABILITY AND IMPLEMENTATION: PyIOmica is implemented as a Python package (pyiomica), available for download and installation through the Python Package Index (https://pypi.python.org/pypi/pyiomica), and can be deployed using the Python import function following installation. PyIOmica has been tested on Mac OS X, Unix/Linux and Microsoft Windows. The application is distributed under an MIT license. Source code for each release is also available for download on Zenodo (https://doi.org/10.5281/zenodo.3548040). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics. Sergii Domanskyi, Carlo Piermarocchi, George Mias |
Bioinform. | 3 |