Data-Driven Insights into Anaerobic Microbes AI for Microbial Analysis and Applications

Anaerobic microbes play a critical role in energy production, waste treatment, and environmental sustainability through processes like methanogenesis, fermentation, and biodegradation. However, analyzing their complex interactions and metabolic potential requires advanced computational methods.
This workshop bridges microbiology and AI by introducing participants to machine learning and deep learning approaches for studying anaerobic microbial consortia. Through hands-on sessions, attendees will work on datasets related to microbial community structure, metagenomic profiling, and metabolite prediction using Python-based tools and AI algorithms.

This workshop aims to explore how Artificial Intelligence (AI) and data-driven approaches can be applied to analyze anaerobic microbial systems for environmental, industrial, and biomedical applications. Participants will gain insights into microbial community modeling, metagenomic data analysis, and AI-driven prediction of metabolic pathways for sustainable bioprocessing and waste management.

Understand the role and diversity of anaerobic microbes in environmental and industrial processes.
Learn data-driven techniques for analyzing microbial community data and metagenomics.
Apply AI/ML algorithms for predicting microbial functions and metabolic pathways.
Explore real-world applications of AI in biogas production, waste treatment, and bioremediation.
Gain hands-on experience with tools like Python, Scikit-learn, QIIME2, and TensorFlow for microbial data analysis.

Day 1: Introduction to Anaerobic Microbes & AI in Microbial Data Analysis

Characteristics of anaerobic microbes and their role in biogeochemical cycles.
Key processes: methanogenesis, fermentation, denitrification, and their relevance in biotechnology.
Applications: Anaerobic microbes in biogas production, bioremediation, and health applications.
Sequencing techniques used to gather microbial data (e.g., 16S rRNA, shotgun sequencing).
Data sources and datasets: Accessing public repositories for microbiome data.
Hands-On Activity: Analyzing a sample metagenomic dataset using Python-based machine learning tools (e.g., scikit-learn, TensorFlow).

Undergraduate/Postgraduate degree in Microbiology, Biotechnology, Bioinformatics, Environmental Science, Computational Biology, or related fields.
Professionals in bioenergy, wastewater treatment, or environmental biotechnology sectors.
Data scientists and AI/ML engineers interested in applying AI to microbial and environmental data.
Individuals passionate about microbial ecology, sustainability, and bioprocess optimization.

Ability to apply AI for analyzing and interpreting microbial community data.
Skills to use machine learning for predicting microbial functions and environmental roles.
Understanding of microbial interactions within anaerobic systems.
Practical experience with data-driven tools for microbial ecology and bioprocessing.
Awareness of industrial and environmental applications of anaerobic microbial AI.

Live & recorded sessions
e-Certificate upon completion
Post-workshop query support
Hands-on learning experience

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Data-Driven Insights into Anaerobic Microbes AI for Microbial Analysis and Applications

Course Overview

Program Syllabus

About This Course

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Lead Instructor

Dr. Sarah Chen