Welcome to DeepBioLab - Exploring the Intersection of AI and Biology
Welcome to DeepBioLab, a platform where I document my journey as an independent developer and AI researcher, diving into the latest advancements in AI and exploring how they can be applied to solve complex biological problems. This blog serves as a hub for sharing my thoughts, research, and projects with the broader AI and biology communities.
Purpose of the Blog
The primary goal of DeepBioLab is to create an open space for learning, experimentation, and collaboration. By sharing my experiments in AI-driven biology, I hope to connect with like-minded researchers and developers who are also passionate about bridging the gap between artificial intelligence and life sciences.
In this blog, you’ll find content ranging from deep dives into neural network architectures, bioprocess modeling, and reinforcement learning, to discussions on large language models (LLMs) and their potential applications in bioinformatics.
What You Can Expect
- Detailed AI-driven biology projects
- Research insights and experiment results
- Tutorials on implementing AI techniques from scratch
- Discussions on the latest trends in AI for science
Upcoming Projects and Blog Posts
In the coming months, I plan to explore the following topics and projects:
- AlphaFold-inspired Protein Structure Prediction from Scratch
- Developing a minimal version of AlphaFold to better understand protein folding mechanisms and applying AI to molecular biology.
- Self-Play in Reinforcement Learning with GPT Agents
- Investigating the application of self-play to language models and potential applications in bioinformatics.
- Large-Scale Language Models for Biological Data Analysis
- Exploring how LLMs can be used to interpret complex biological datasets, and how they can assist in predictive modeling.
- LLMs Paper Reviews
- In this series, I’ll be reviewing key papers on Large Language Models (LLMs), focusing on their architecture, advancements, and potential applications in scientific research, especially in biology. This section will offer in-depth discussions on groundbreaking work like GPT, BERT, and other transformative LLM models.
- Andrej Karpathy’s NN Zero to Hero — The Science Version
- Inspired by Andrej Karpathy’s famous “NN Zero to Hero” series, I’m working on adapting this educational format specifically for applications in scientific fields. This version will aim to introduce neural networks from the ground up and explore how these principles can be applied to scientific research, with examples from biology and chemistry.
- Bayesian Optimization in Bioprocess Modeling
- Bayesian optimization has proven to be a powerful tool for optimization problems where evaluation is expensive. In this section, I will demonstrate how Bayesian optimization can be used in bioprocess modeling, offering an effective approach to fine-tuning biological models and experiments for maximum efficiency and performance.
Stay tuned! By subscribing to this blog, you’ll receive updates whenever a new project or research article is published. I invite you to join me on this journey as I continue to push the boundaries of what’s possible at the intersection of AI and biology.
Thank you for being part of this journey. If you’re as passionate about AI-driven biology as I am, feel free to reach out via Email or GitHub. I look forward to sharing more exciting research and ideas with you.
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