From Petri-Dish to Predictions – AI Meets Microbiology
Harnessing AI to Decode the Invisible World of Microbes
Skills you will gain:
About Program:
Microbiology has entered a new era where petri-dish observations alone are no longer sufficient to keep up with the scale of biological data. From imaging colony growth to sequencing microbial communities, researchers now face an influx of complex data requiring advanced analysis. This workshop explores how AI techniques such as machine learning, computer vision, and predictive analytics are revolutionizing microbiology. By introducing participants to tools and methodologies, the workshop empowers researchers to build models that classify microbes, predict resistance, and decode microbial patterns from genomic and image data.
Over the course of three days, participants will explore real-world case studies, hands-on demonstrations, and expert-guided discussions. Topics will span microbial image recognition, genome-based predictions, microbiome analytics, and the use of AI in environmental, industrial, and clinical microbiology. Whether you’re a biologist aiming to embrace AI or a data scientist venturing into microbiology, this workshop will serve as your cross-disciplinary launchpad.
Aim: This 3-day workshop aims to bridge the gap between microbiology and artificial intelligence, enabling participants to apply AI-based tools to microbial data analysis, prediction, and innovation. It introduces interdisciplinary insights into microbial imaging, genomics, metagenomics, and microbiome analysis using AI/ML techniques. Participants will gain the knowledge and skills to utilize computational tools for microbial diagnostics, environmental applications, and health forecasting. This workshop builds a foundation for data-driven research and industry applications in microbiology.
Program Objectives:
- Understand the intersection of AI and microbiology, from image-based recognition to genomic data mining
- Gain hands-on exposure to microbial image classification and machine learning workflows
- Explore AI tools for microbiome analysis and epidemiological predictions
- Learn to implement supervised/unsupervised models for microbial datasets
- Discover real-world case studies in health, agriculture, and environmental microbiology
What you will learn?
Day 1: AI-Driven Microbial Imaging & Pattern Recognition
- Overview of microbiology: culture, colony morphology, and AI applications.
- Deep learning for microbial colony recognition.
- Detecting resistance from culture plate images using CNNs.
- Tools: TensorFlow, Keras for image-based data.
- Case Study: AI in Antibiotic Resistance
- Exercise: Image classification with deep learning.
Day 2: Advanced Microbial Genomics & Predictive Modeling
- Techniques: NGS, 16S rRNA, metagenomics.
- Predicting diversity and pathogen identification with AI.
- Predictive Modeling of Microbial Communities
- Predicting microbial diversity using machine learning.
- Exercise: Supervised learning for genomic classification.
- Tools: Scikit-learn, Biopython, Kraken2 for genomic analysis.
Day 3: AI in Microbiome, Epidemiology & Applications
- Gut health, probiotics, and disease prediction.
- Tools: QIIME2, TensorFlow.
- Disease outbreak prediction and epidemiological forecasting.
- Exercise: AI model for disease prediction.
- AI in fermentation, biosensors, and pollution prediction.
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
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Undergraduate/postgraduate degree in Microbiology, Biotechnology, Bioinformatics, Computational Biology, Environmental Science, or related fields.
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Professionals in healthcare, pharma, diagnostics, food safety, or environmental sectors.
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Data scientists and AI/ML engineers interested in applying their skills in biological and healthcare domains.
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Individuals with a keen interest in the convergence of life sciences and artificial intelligence.
Career Supporting Skills
Program Outcomes
- Strong conceptual understanding of AI’s application in microbiology
- Ability to preprocess, analyze, and model microbial datasets
- Hands-on knowledge of microbial image and genomic data pipelines
- Proficiency in tools like QIIME2, Python, Biopython for bio-AI integration
- Readiness to explore AI-driven roles in bio R&D and diagnostics