A book build through chatGPT prompts to become an expert at ML+Bio intersection. The book is guided through series of prompts. I like Oreilly books and generally books or courses that provide a quick overview of all key concepts related to a particular topic. The author or course instructors typically spend hours and hours of their time by providing effective curriculum or concise material. Over last couple of years, I am thinking about various topics and zoomed in at the intersection of Machine Learning and Biology. I tried to find books and courses around the topic. This area is changing rapidly and new research is coming out every day. I did find Machine Learning courses from Andrew Ng and Jeremy Howard (Both Deep learning specialization and fast.ai courses) and decided to start Deep learning specialization.

At the same time, natural language processing research is changing rapidly with ChatGPT. People started talking about various things such as Artificial general intelligence, ethics, jobs, Auto GPT or baby GPT and so many new things every day. Then I started thinking about how to use ChatGPT.

One thing I found interesting is humans ability to ask questions and reason with the avaialble data and conduct experiments. But asking the right questions is hard when you dont know the fundamental concepts to quickly get started. So, I decided to use ChatGPT to write top sections to include in a book called "Getting Started with Biology and ML" and then ask ChatGPT to write 3 prompts for each of the 10 sections and their summaries. There is always a problem about random text generated by ChatGPT. For example, I asked ChatGPT to provide 2-3 coding exercises for each of the prompts with github or tutorial links and of course it randomly made things up.

But getting the 30 prompt questions for the 10 sections allowed me to get quick overview of the entire topic.

First I asked chatgpt to provide top 10 sections of a book that will give me an overview. After some prompt engineering, I got the following which are good topics to get a good grip on.

This section provides an overview of the two fields and explores the intersection of biology and machine learning. It highlights the importance of this intersection and the potential impact it can have on various industries.

This section covers the fundamental concepts of biology such as genetics, biochemistry, and cellular biology. It aims to provide a solid foundation of biological concepts necessary for understanding the application of machine learning in biology.

This section introduces the fundamental concepts of machine learning such as supervised and unsupervised learning, decision trees, and neural networks. It also covers the mathematical foundations of machine learning necessary for understanding the application of machine learning in biology.

This section explores the various applications of machine learning in biology, including drug discovery, disease diagnosis, and gene expression analysis. It also discusses the challenges and limitations of applying machine learning in biology.

This section covers the various machine learning techniques used in genomics, including sequence alignment, gene prediction, and variant calling. It also discusses the challenges of applying machine learning in genomics.

This section covers the various machine learning techniques used in proteomics, including protein identification, quantification, and structure prediction. It also discusses the challenges of applying machine learning in proteomics.

This section covers the various machine learning techniques used in systems biology, including network analysis, pathway analysis, and systems modeling. It also discusses the challenges of applying machine learning in systems biology.

This section covers the various machine learning techniques used in neuroscience, including neuroimaging analysis, neural network modeling, and brain-computer interfaces. It also discusses the challenges of applying machine learning in neuroscience.

This section discusses the ethical considerations involved in using machine learning in biology, including issues related to privacy, bias, and fairness. It also explores responsible ways of using machine learning to advance the field of biology.

This section explores the future directions of the intersection of biology and machine learning. It discusses the potential impact of new technologies and techniques on the field and the challenges that need to be addressed to realize the full potential of this intersection. Then I asked ChatGPT to write 3 prompts for each section and provide 2-3 sentences summary. I had problems getting all sections answered in one prompt and needed to do it 3 times. Overall it worked out.

I will update these after doing some prompt engineering and verify the summaries.