| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ~Get Your Files Here ! | |||
| 1 - The AI Revolution in Bioinformatics | |||
| 1. Welcome & Course Overview.mp4 | 36.7 MB | ||
| 2 - Prompt Engineering (The Only Theory You Need) | |||
| 10. Role-Based Prompting for Scientific Tasks.mp4 | 47.6 MB | ||
| 11. Context Injection (How to Give AI “Brain”).mp4 | 47.2 MB | ||
| 12. Structured Outputs (Tables, Steps, JSON-style thinking).mp4 | 41.1 MB | ||
| 13. Zero-shot vs Few-shot (When to use what).mp4 | 45.3 MB | ||
| 14. Iterative Prompting (Fixing bad outputs).mp4 | 39.6 MB | ||
| 15. Reducing Hallucinations in Biology.mp4 | 36.3 MB | ||
| 16. Prompt Debugging Framework.mp4 | 35.2 MB | ||
| 3 - Literature, Research & Understanding Papers | |||
| 17. Summarizing Research Papers Properly.mp4 | 66.9 MB | ||
| 18. Extracting Methods, Results & Key Insights.mp4 | 47.2 MB | ||
| 19. Explaining Complex Biological Concepts.mp4 | 24.3 MB | ||
| 20. Comparing Multiple Papers.mp4 | 29.4 MB | ||
| 21. Generating Literature Review Structures.mp4 | 28.2 MB | ||
| 22. Identifying Research Gaps (Very valuable lecture).mp4 | 34.2 MB | ||
| 23. Turning Papers into Presentations.mp4 | 45.1 MB | ||
| 24. Validating AI Output in Scientific Context.mp4 | 51.7 MB | ||
| 4 - Bioinformatics Workflows & Pipelines with AI | |||
| 25. Designing NGS Pipelines with AI.mp4 | 29.4 MB | ||
| 26. RNA-seq Workflow (Prompt → Output → Fix).mp4 | 24.2 MB | ||
| 27. Variant Calling Pipelines.mp4 | 50.6 MB | ||
| 28. Metagenomics Workflow Design.mp4 | 49.3 MB | ||
| 29. Tool Comparison Prompts (e.g., BWA vs Bowtie).mp4 | 46.8 MB | ||
| 30. Converting Experimental Design into Pipelines.mp4 | 58.3 MB | ||
| 31. Generating Workflow Diagrams (conceptual).mp4 | 23.7 MB | ||
| 5 - Coding with AI for Bioinformatics | |||
| 32. Prompting for Python Scripts (Bioinformatics-focused).mp4 | 81.6 MB | ||
| 33. Prompting for R (DESeq2-style thinking, not tool-specific).mp4 | 39.2 MB | ||
| 34. Writing Bash Pipelines.mp4 | 57.8 MB | ||
| 35. Debugging Code with AI.mp4 | 28.1 MB | ||
| 36. Improving Performance & Efficiency.mp4 | 29.5 MB | ||
| 37. Writing Custom Functions.mp4 | 56 MB | ||
| 38. Explaining Code Line-by-Line.mp4 | 46.2 MB | ||
| 39. Making Code Reproducible.mp4 | 26.3 MB | ||
| 6 - Data Interpretation, Writing & Communication | |||
| 40. Interpreting Bioinformatics Results.mp4 | 73.3 MB | ||
| 41. Explaining PCA, Clustering, Heatmaps.mp4 | 29.8 MB | ||
| 42. Writing Figure Legends.mp4 | 55.4 MB | ||
| 43. Converting Results into Paper Text.mp4 | 59.1 MB | ||
| 44. Writing Research Papers with AI.mp4 | 63.4 MB | ||
| 45. Writing Abstracts & Introductions.mp4 | 34.2 MB | ||
| 46. Improving Scientific Writing Style.mp4 | 53.1 MB | ||
| 47. Maintaining Your Scientific Voice.mp4 | 53.6 MB | ||
| 7 - Advanced Usage & Real-World Strategy | |||
| 48. Multi-step Prompting (Complex Tasks).mp4 | 130.6 MB | ||
| 49. Building a Personal Prompt Library.mp4 | 76.4 MB | ||
| 50. Using AI as a Research Assistant.mp4 | 27.2 MB | ||
| 51. Hypothesis Generation with AI.mp4 | 92 MB | ||
| 52. Future of AI in Computational Biology.mp4 | 106 MB | ||
| 9. What Makes a Good Prompt (Core Principles).mp4 | 63.8 MB | ||
| 2. What is Generative AI.mp4 | 90.7 MB | ||
| 3. What is Prompt Engineering.mp4 | 92.4 MB | ||
| 4. Large Language Models Explained Simply.mp4 | 88.2 MB | ||
| 5. How AI Understands Biological Language.mp4 | 97.8 MB | ||
| 6. Why Bioinformatics is Perfect for AI Assistance.mp4 | 86.9 MB | ||
| 7. Limitations of AI in Scientific Domains.mp4 | 119.4 MB | ||
| 8. When NOT to Use AI.mp4 | 76 MB |
AI Prompt Engineering for Bioinformatics
https://WebToolTip.com
Published 4/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 8h 14m | Size: 2.81 GB
Master the art of communicating with AI for scientific research
What you'll learn
Writing effective prompts tailored for bioinformatics tasks
Generating, understanding, and debugging Python scripts using AI
Designing workflows for RNA-seq analysis, variant calling, and metagenomics
Converting experimental designs into structured computational pipelines
Comparing bioinformatics tools and selecting the most suitable approach
Extracting key insights from research papers efficiently
Identifying research gaps and generating new research ideas
Creating publication-quality figures and improving data visualization
Validating AI-generated outputs to ensure scientific accuracy
Requirements
A basic understanding of biology or bioinformatics concepts (e.g., DNA, RNA, sequencing, or gene expression)
Familiarity with fundamental computer usage (file handling, installing software, etc.)
Optional but helpful: basic knowledge of Python or any programming language
Access to a computer with an internet connection
Willingness to learn, experiment, and think critically about AI-generated outputs
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