With the rising number of sequenced genomes, fungal omics are now applied to understand the complex and constantly evolving cell-cell (microbial) communications as well as host-pathogen interaction in fungi. However, these interactions are governed by a complex interplay of biological and environmental factors and modern computational algorithms based on Artificial Intelligence (AI) and machine learning (ML) can help in identifying these large scale multi-faceted factors at a much faster pace. These computational strategies, including network biology, also provide a platform for integrative analysis of the large-scale multi-omics datasets to elucidate previously unknown components of host-pathogen interaction and microbial cell-cell communications. Moreover, these advanced algorithms aids in the prediction of novel virulence factors and drug targets as well as design and optimization of drug candidates to help mycologists in designing disease management projects with higher efficacy.

This Special issue aims to collect original research articles and reviews that use modern cutting-edge computational workflows for advancing the research in the field of fungal biology. This includes but is not limited to topics such as:

• Molecular basis of infection and pathogenesis using fungal omics (including genomics, transcriptomics, proteomics lipidomics and metabolomics).

• Integrative analysis of fungal multi-omics datasets.

• Database or data mining tools for host-pathogen interaction and microbial cell-cell communications.

• Novel computational strategies for understanding host-pathogen interactions and microbial cell-cell communications.

• Machine learning and artificial intelligence for identification of Virulence factors.

• Molecular basis of antifungal drug resistance.

• Computational prediction of novel antifungal drug targets.

• Fungal diseases and epidemiology.

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Frontiers in Fungal Biology

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