Overview: Dr Uzma Uzma delivered an oral presentation of her research paper, “Neural network-driven nonlinear analysis of beta diversity in microbial communities with DeepBeta,” at the 12 th International Conference on Bioinformatics and Biomedical Engineering (ICBBE 2025). In addition to the live presentation, this work underwent a rigorous peer-review process and was formally accepted for publication in the conference proceedings.

Intended Purpose: The primary objective of this activity was to introduce a novel computational framework, DeepBeta, to the bioinformatics community. Traditional methods for analyzing microbial beta diversity often rely on linear assumptions that fail to capture the true complexity of biological ecosystems. By utilizing a neural network-driven approach, this research provides a more robust method for identifying nonlinear patterns in microbial communities, which is critical for advancing our understanding of microbiome-host interactions and disease diagnostics.

Academic Contribution: The research was validated through peer acceptance and will be archived in the conference proceedings, providing a new methodological benchmark for researchers in metagenomics and deep learning.

Outcomes and Impacts:
Academic Contribution: The research was validated through peer acceptance and will be archived in the conference proceedings, providing a new methodological benchmark for researchers in metagenomics and deep learning.
Dissemination and Engagement: The oral session allowed for direct engagement with international experts in biomedical engineering.

Professional Networking: The activity resulted in several follow-up inquiries and networking connections with global scholars, potentially leading to future collaborative research on deep learning applications in environmental and clinical microbiology.