What makes us different
Artificial Intelligence (AI) is rapidly transforming the biological sciences, opening up new possibilities in research, drug discovery, diagnostics and personalized medicine. From decoding human genome to predicting disease outbreaks and designing personalized treatments, AI is transforming how we understand, analyse and apply biological knowledge. Our artificial intelligence in biology training program is structured to equip with the skills and knowledge nee to thrive in the rapidly growing field. We designed our curriculum to be accessible, focused and completely practical with core biological challengers with AI concepts and tools to solve them. Our coaching empowers you to apply AI tools to real biological problems, complex datasets and innovation in healthcare and research.
Our Genomic Resolve Research Centre offer specialized training and hands on experience in artificial intelligence in biology which combines the depth of science knowledge with machine learning and data analytics. We help you to apply these skills in research, pharmaceutical and healthcare industries.
Who can apply?
- B.Sc/M.Sc/B.Tech/M.Tech students or graduates in Biology, Pharmacy, Biochemistry, Microbiology, Biotechnology, Biomedical, Data Science, Molecular Biology, Bioinformatics, Genetics and other life science related fields.
- PhD scholars who want to gain skills in AI and biological data analysis.
- Working professionals seeking skills to upgrade in AI and biomedical data science.
What you will learn?
Introduction to AI and Its Relevance in Biology
Overview of AI, machine learning (ML), and deep learning.
Applications in biology: genomics, proteomics, drug discovery, and imaging.
Case studies: AI in protein folding (e.g., AlphaFold), disease prediction.
Reading: Review papers on AI in biology (e.g., Nature reviews).
Fundamentals of Machine Learning for Biology
Supervised vs. unsupervised learning, neural networks, decision trees.
Data preprocessing: handling biological datasets (e.g., genomic sequences, imaging data).
Tools: Python, R, TensorFlow, scikit-learn.
Practical: Install and explore Python libraries for ML.
AI in Genomics and Proteomics
AI for sequence analysis, variant calling, and gene expression.
Predictive modeling for protein structure and function.
Practical: Analyze a genomic dataset using a simple ML model (e.g., classification of gene variants).
AI in Drug Discovery and Systems Biology
AI in virtual screening, molecular property prediction, and network biology.
Tools: DeepChem, RDKit for cheminformatics.
Practical: Build a model to predict drug-target interactions.
Ethics, Challenges, and Future Directions
Bias in AI models, data privacy, and ethical considerations.
Limitations: data quality, interpretability, computational resources.
Future trends: AI in personalized medicine, synthetic biology.
Assignment: Write a short proposal on an AI application in biology.
Course Features
Experts in AI and Life Science: Our training team incudes professionals in biology, bioinformatics, data science and machine learning. Our candidates learn form our experts who work in these fields and support with biological problems and technical solutions.
Hands on Training: We provide practical coaching in analysing biological data and support project-based learning for our candidates.
Flexible Batch and Duration: We offer online, self-learning and offline training by our candidate’s schedule and support 15 days to 3 months duration for artificial intelligence coaching based on their convenience.
Accommodation Facilities: For outstation candidates we offer comfortable and affordable support for accommodation.
Certification: Receive the artificial intelligence in biology certification by highlighting the tools and techniques you have learnt which will become valuable credential for your career development.
