Learn Generative AI for Bioinformatics and Life Sciences

Practical AI-Driven Workflows for Genomics, RNA-seq, Variant Analysis, and Scientific Research
What you'll learn
✓ Understand how Generative AI and Large Language Models (LLMs) work and how they can be applied safely in bioinformatics and life-science research.
✓ Distinguish clearly between tasks where AI is useful (planning, interpretation, reporting) and tasks where AI must not be used (alignment, statistical computati
✓ Design complete, reproducible bioinformatics pipelines (RNA-seq, ChIP-seq, and variant analysis) using AI-assisted Linux workflows.
✓ Generate, validate, and debug Bash and Python scripts with AI support while maintaining full human oversight and scientific accuracy.
✓ Interpret RNA-seq, ChIP-seq, and variant analysis results biologically using structured AI prompting without making unsafe or clinical claims.
✓ Convert bioinformatics outputs into professional scientific reports, including Methods, Results, Discussion sections, figure legends, and summaries.
✓ Build reusable prompt libraries for pipeline generation, debugging, interpretation, and reporting in bioinformatics projects.
✓ Identify and prevent AI hallucinations, incorrect biological claims, and reproducibility risks in scientific workflows.
✓ Apply ethical, transparent, and journal-compliant practices when using AI in academic research and publications.
✓ Design biologically meaningful experiments and hypotheses using AI support while respecting experimental, statistical, and ethical constraints.
✓ Use AI to assist in grant writing, proposal drafting, and methodology justification while maintaining scientific credibility.
✓ Integrate AI effectively with Linux, Python, and bioinformatics tools in real-world research environments.
Requirements
● This course is designed to be beginner-friendly, and no advanced programming or AI background is required.
● Basic understanding of biology or life sciences (molecular biology, genetics, or biotechnology concepts)
● Familiarity with NGS terminology (e.g., FASTQ, BAM, RNA-seq) is helpful but not required
● Very basic computer usage skills (working with files, folders, and software)
Description
Artificial Intelligence is rapidly transforming bioinformatics but using AI correctly, safely, and effectively in biological research requires more than just asking questions.
This course is a complete, practical guide to using AI responsibly in bioinformatics and genomics, with a strong focus on RNA-seq, variant analysis, pipeline development, interpretation, and scientific reporting. You will learn where AI truly adds value, where it must not be trusted, and how to combine AI with Linux, Python, and standard bioinformatics tools to accelerate real research workflows.
Instead of replacing bioinformatics tools, this course teaches you how to use AI as a research assistant for experimental design, pipeline generation, debugging, biological interpretation, hypothesis development, and professional scientific writing while maintaining reproducibility, accuracy, and ethical integrity.
Through hands-on examples, real-world case studies, structured prompt libraries, and capstone projects, you will build AI-assisted workflows that reflect how modern bioinformatics research is actually conducted in academia and industry.
This is not a theory-only course.
You will work with
• Real RNA-seq and genomics scenarios
• Step-by-step prompting examples
• Pipeline generation and debugging exercises
• Interpretation of real biological outputs
• AI-assisted report writing
• Case studies reflecting real research workflows
You will see how AI behaves with good prompts vs poor prompts, how hallucinations appear, how errors emerge, and how to systematically detect and correct them.
The goal is not automation for automation's sake the goal is professional-grade bioinformatics practice.
Whether you are a student, researcher, or professional, this course equips you with future-ready skills to work faster, think more clearly, and communicate your bioinformatics results with confidence without sacrificing scientific rigor.
This course does not promise shortcuts. It promises clarity, structure, responsibility, and professional growth.
If you want to use AI the right way in bioinformatics with confidence, ethics, and scientific credibility, this course is built for you.
Who this course is for
■ Students in bioinformatics, biotechnology, genomics, molecular biology, or life sciences who want to apply AI in real research workflows
■ Beginner to intermediate bioinformaticians who want to safely integrate AI into RNA-seq, ChIP-seq, and variant analysis pipelines
■ Wet-lab researchers seeking to understand bioinformatics analysis, result interpretation, and AI-assisted reporting without becoming full-time programmers
■ Graduate students and PhD researchers who want to accelerate data analysis, writing, and hypothesis generation using responsible AI practices
■ Industry professionals in genomics, transcriptomics, or computational biology who want to upskill in AI-driven workflows
■ Educators and trainers looking to teach AI-assisted bioinformatics in a structured, ethical, and reproducible way
Homepage

https://www.udemy.com/course/learn-generative-ai-for-bioinformatics-and-life-sciences/

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