NP26 Applied AI in Biotechnology
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Why AI?
AI is revolutionizing the biotech industry by enabling the analysis of complex datasets beyond human capability, accelerating drug discovery, and paving the way for personalized medicine. Its urgent integration stems from the need to meet the growing demands for healthcare solutions, making breakthroughs faster and more cost-effective.
For any professional in this sector, this playbook will get you started on this urgent and inevitable journey. Don’t miss out on the AI revolution – download the paper, read and apply.
YOU WILL LEARN:
- Gene Sequencing Analysis: AI for faster, cheaper genetic insights.
- Predictive Modeling in Drug Development: Identifying potential compounds swiftly.
- CRISPR Efficiency Optimization: Enhancing gene-editing outcomes.
- Protein Folding Predictions: Understanding protein structures for new therapies.
- Cell Image Analysis: Automating disease marker identification.
- Biomarker Discovery: Accelerating identification of disease indicators.
- Synthetic Biology: Designing synthetic organisms for pharmaceuticals or agriculture.
- Clinical Trial Optimization: Improving patient selection and monitoring.
- Regenerative Medicine: Aiding in tissue engineering and organ regeneration.
- Environmental Biotechnology: AI for bio-remediation and sustainable practices.
FREE RESOURCES:
SOME FLASHCARDS
(PDF, 24 pages, 10×10)
This PDF captures the main ideas from the paper. Easily shareable on SoMe for a start of an online conversation about AI in your industry.
CHEAT SHEET
(PDF, 2 pages, A4)
This PDF contains the main ideas from the paper, for your easy reference.
USE CASES
(PDF, 16 pages, 4×3)
This PDF contains an overview of the 10 main AI use cases for this industry.
To get started with AI, first do this:
- Invest in AI Training: Equip your team with the skills to leverage AI tools.
- Collaborate with AI Innovators: Partner with AI research institutions and startups.
- Focus on Data Quality: Prioritize the acquisition and management of high-quality, annotated biological data sets for AI applications.
- Explore other resources on nextpaper.me and check out the book “Explain for me AI”.
- Please contact us at hello@nextpaper.me for further exploration or inspiration through a talk, workshop or case study. We’d love to help!
Silvija Seres
Mathematician & AI Investor
SILVIJA SERES - Mathematician and AI investor
I have worked with AI for more than 30 years in research, development and strategy. I am very interested in helping companies drive successful digital transformation and AI applications. If you find this interesting, please get in touch on hello@nextpaper.me
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