Automated analysis of microscopic image data of host-pathogen interaction
SEEK ID: https://funginet.hki-jena.de/people/172
Location:
Germany
ORCID:
https://orcid.org/0000-0002-1828-8351
Joined: 12th Feb 2021
Expertise: Not specified
Tools: deep learning, Machine Learning, Image analysis
Related items
The yeast Candida albicans and the filamentous fungus Aspergillus fumigatus are by far the most important causes of life-threatening invasive mycoses in Europe. Despite the increasing incidence of these infections, the current diagnosis is still difficult and often too late, and options for therapies are limited. Moreover, A. fumigatus and C. albicans have developed multiple sophisticated, specific and unique pathogenicity mechanisms. Many of these mechanisms are not well understood. Research on ...
Projects: FungiNet total, FungiNet A - Aspergillus projects, FungiNet B - Bioinformatics projects, FungiNet C - Candida projects, INF, A1, A2, A3, A4 (E), A5, B1, B2, B3 (E), B4, C1, C2, C3, C4 (E), C5, C6 (E), Z2, Z1, B5, A6, A7, A8, C7
Web page: http://www.funginet.de/home.html
Image data analysis and agent-based modelling of the spatio-temporal interaction between immune cells and human-pathogenic fungi
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