Prevalence Leveraged Consistent Feature Selection Decodes Microbial Signatures across Cohorts

Date:  2024/12/05 (Thursday) 10:00-11:00 AM

Online lecture (The Microsoft Teams link will be sent to your email address the day before the speech)

Abstract:

The intricate nature of microbiota sequencing data,—high dimensionality and sparsity—presents a challenge in identifying informative and reproducible microbial features for both research and clinical applications.

Addressing this, we introduce PreLect, an innovative approach that harnesses microbes' prevalence to facilitate consistent selection in sparse microbiota data.

Highlights:

1. PreLect outperformed both statistical methods and machine learning-based methods in feature selection across 42 microbiome datasets.

2. Ability to reliably identify reproducible microbial features across varied cohorts.

3. PreLect identifies key microbes and highlights crucial pathways in cancer progression.

4. PreLect’s accuracy and robustness make it a significant advancement in the analysis of microbial signatures in complex microbiota data.

Registration:  https://forms.gle/NVMjwuQajawebMA29

Contact person: Ms. Chen helenchen125@nycu.edu.tw

*Deadline of registration: 2024/12/04 12:00   (GMT+8)