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George Mias

dblp:250/6775 · also George I. Mias · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › omics data analysis
longitudinal omics analysis
0.412020
PyIOmica: longitudinal omics analysis and trend identification · Bioinform. 2020

Methods — techniques the papers use, named apart from their topics

visibility graph · 0.4
YearPublicationVenuePosition
2020 PyIOmica: longitudinal omics analysis and trend identification
abstract
SUMMARY: 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