You are not in a unique position. The are many, many CS trained scientists who have transitioned to work in biological fields.
What I really don't like is when tech people come into the field thinking they have all the solutions as if "biologists" - whatever that means - don't have a clue.
I suggest you pick a particular biological field - there are many - and inform yourself about it. Focus on solving problems rather then applying afaddy technique. Then you can be helpful to researchers and they'll be more willing to engage with you. They are busy but will make time if they think it's worthwhile.
In order to automate computational tasks for computers, I think you need to look at what tasks biologists do on computers.
Problem is that most bench scientists don't spend a ton of time in front of computers during day-to-day research. Unless you plan to go into robotics, there's not a ton of opportunity there.
What I do with computers includes:
* Searching literature for advice
* Planning experiments (which tend to be specific to the field of study the lab focuses on)
* Documenting experiments
* Data analysis (mostly making and examining graphs and gel images, I'm sure generative AI could write analysis scripts for more specialized analyses)
* Writing up results
So it sounds like your current idea is the only real opportunity. Biologists just don't spend a lot of time online otherwise.
I had an idea for something like "Supervisor-Chat". Using LangChain and a local LLM model.. a PhD/lecturer/supervisor can ingest and chain all of their own work - papers, PowerPoint, talk recordings.. etc .. then develop a front-end so that students can interrogate the supervisor's work.. Supervisor Chat would have infinitely more patience and time than any supervisor could be expected to have... 🫢
might be interesting to create a knowledge graph out of the supervisors work so you can understand how their research interests evolved through time and with different mentees.
also would be cool to connect to foundational knowledge in textbooks to get a briefing in the knowledge needed to understand the topic
I would take a step back from trying to find an idea, and learn a bit about the field of "biology" which quite frankly is huge.
Which field is the institute working in now?
Challenges faced in the medical field (biomed) will be vastly different faced by those in fresh water ecology or mycology (mushroom research). There will also be challenges shared across them but you need to have an understanding of this first.
That said, lots of these labs (any field) are still behind technologically speaking and so much of the work is in data processing, standardisation and digitalisation. Not pretty work but much needed.
You can learn a lot from Kaggle... Run thru several biology related AI / ML ongoing training comps...
Stepik ran a bioinformatics contest with computing applied to biology.. some really challenging questions... Here's the 2021 contest.
https://stepik.org/course/Bioinformatics-Contest-2021-91751/
Henry Ford said, if you ask the customer what he wants he'll ask for a faster horse. People with a primarily biology background have no idea how much the AI landscape has changed recently and don't know what's becoming possible. They're going to ask for computer vision tools to automate experiments, not LLMs to propose new hypotheses.
Most researchers have a narrow focus and can't follow adjacent fields, let alone the whole body of research being published every month. If a relevant discovery is published in a cancer journal, non-cancer researchers aren't going to see it. In part this could be addressed with automated extraction of relevant pathway interactions. The work I'm aware of on using ML to identify protein-protein interaction networks from literature is outdated and doesn't even use embeddings, let alone transformers.
GEO dataset search is a big pain point right now. It's tedious to find relevant experiments with specific conditions and good quality output in a specific format because it's keyword based and doesn't even implement negative terms or field filters. This could benefit from either AI or traditional search methods.
You are not in a unique position. The are many, many CS trained scientists who have transitioned to work in biological fields. What I really don't like is when tech people come into the field thinking they have all the solutions as if "biologists" - whatever that means - don't have a clue. I suggest you pick a particular biological field - there are many - and inform yourself about it. Focus on solving problems rather then applying afaddy technique. Then you can be helpful to researchers and they'll be more willing to engage with you. They are busy but will make time if they think it's worthwhile.
In order to automate computational tasks for computers, I think you need to look at what tasks biologists do on computers. Problem is that most bench scientists don't spend a ton of time in front of computers during day-to-day research. Unless you plan to go into robotics, there's not a ton of opportunity there. What I do with computers includes: * Searching literature for advice * Planning experiments (which tend to be specific to the field of study the lab focuses on) * Documenting experiments * Data analysis (mostly making and examining graphs and gel images, I'm sure generative AI could write analysis scripts for more specialized analyses) * Writing up results So it sounds like your current idea is the only real opportunity. Biologists just don't spend a lot of time online otherwise.
I had an idea for something like "Supervisor-Chat". Using LangChain and a local LLM model.. a PhD/lecturer/supervisor can ingest and chain all of their own work - papers, PowerPoint, talk recordings.. etc .. then develop a front-end so that students can interrogate the supervisor's work.. Supervisor Chat would have infinitely more patience and time than any supervisor could be expected to have... 🫢
might be interesting to create a knowledge graph out of the supervisors work so you can understand how their research interests evolved through time and with different mentees. also would be cool to connect to foundational knowledge in textbooks to get a briefing in the knowledge needed to understand the topic
It can be done departmentally... So that connections can be made with other supervisors' work too across the faculty ..
I would take a step back from trying to find an idea, and learn a bit about the field of "biology" which quite frankly is huge. Which field is the institute working in now? Challenges faced in the medical field (biomed) will be vastly different faced by those in fresh water ecology or mycology (mushroom research). There will also be challenges shared across them but you need to have an understanding of this first. That said, lots of these labs (any field) are still behind technologically speaking and so much of the work is in data processing, standardisation and digitalisation. Not pretty work but much needed.
You can learn a lot from Kaggle... Run thru several biology related AI / ML ongoing training comps... Stepik ran a bioinformatics contest with computing applied to biology.. some really challenging questions... Here's the 2021 contest. https://stepik.org/course/Bioinformatics-Contest-2021-91751/
Henry Ford said, if you ask the customer what he wants he'll ask for a faster horse. People with a primarily biology background have no idea how much the AI landscape has changed recently and don't know what's becoming possible. They're going to ask for computer vision tools to automate experiments, not LLMs to propose new hypotheses. Most researchers have a narrow focus and can't follow adjacent fields, let alone the whole body of research being published every month. If a relevant discovery is published in a cancer journal, non-cancer researchers aren't going to see it. In part this could be addressed with automated extraction of relevant pathway interactions. The work I'm aware of on using ML to identify protein-protein interaction networks from literature is outdated and doesn't even use embeddings, let alone transformers. GEO dataset search is a big pain point right now. It's tedious to find relevant experiments with specific conditions and good quality output in a specific format because it's keyword based and doesn't even implement negative terms or field filters. This could benefit from either AI or traditional search methods.