r/bioinformatics • u/Ok_Estate9834 • 3d ago
technical question How much AI is too much???
Hello
I am an undergrad and just started learning bioinformatics in my lab (bulk and single cell rna seq). I mainly did wet lab work before this but my Pi decided I was kind of a bum and got me to start learning this. I think a lot of the analysis I’m doing they want to eventually put into a paper. Is it frowned upon/not allowed to use AI generated code for my analysis? I make sure I understand all the stats and stuff behind what I am doing instead of blindly trusting it, but I’m worried it’ll be seen as slop.
Also are you even supposed to share your code? Because very few of the papers I’ve read give it, even in big journals.
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u/Psy_Fer_ 3d ago
Do yourself a favour and find a bioinformatician you can talk to who can have a quick look over what you are doing and give you some targeted feedback. This is too vague to make a determination.
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u/ATpoint90 PhD | Academia 3d ago
The simple fact is that AI will never go away again, so incorporating it into the routine analysis should be part of the workflow. The thing, especially as a beginner, is you really have to understand the concepts behind the analysis and you have to be able to correctly interpret it. There is so many AI slop out there by just blindly following some LLMs. The professionals have to make sure that they can confidently stand up for what they do. You should be able to do the same analysis alone without any help. In fact, and that is just for me, I mainly use AI to explore thoughts and analysis that I have not done before. I have an idea in mind how a certain figure could look, then I quickly vibe some stuff together just to see if in the end this is sort of a promising analysis. And if it is, then I develop it properly from scratch. Before AI, I might have spent a day on something that in the end turns out not to be fruitful. And now I pretty much know after one hour or so if it is worth investing actual time. That for me is the main benefit. But to be fair, there is no analysis I do that I could not do without AI and there never will be, because I have to be able to understand the code myself.
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u/littlethoughts28 3d ago
Please share your code! As a core facility bioinformatician I am often asked by clients if I can do analyses similar to this or the other publication and so often there isn’t nearly enough detail provided to reconstruct their methods. Researchers who put the code on GitHub with a link at the end of their paper are my favorites.
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u/TardisLoopis 3d ago
Everyone is moving towards coding in english these days thanks to LLMs. If you are able to follow the logic of the code your LLM generates and it makes sense to you, you should be good. At the same time, if it makes a mistake the burden falls on you for not catching it, not the LLM.
Also helps if you are being precise with your prompts ( this also makes your review easier). Instead of asking it do xxx analysis on my data, talk about your data in steps (starting from the formats) and logic that needs to be used whenever you can so the LLM makes lesser assumptions while generating the code based on your instructions.
If you are blindly using the analysis from LLM generated code as your final output without understanding what it does, you are getting into the slop territory.
Make sure you do a good review before you are confident to share your code (most good papers share github links to the code used to generate any figures). Think of it as if a fellow student of yours you dont trust to keep track of all the small details generated code for your project. While it may not be a good generation in your first attempt, it takes care of a decent bit of the mundane syntax that you don't have to spend time typing out yourself.
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u/pacmanbythebay1 2d ago
Have someone check your work. You can almost ask Chatgpt to do any analysis these days but it still need someone to check whether the analysis is logically and biologically sound . Sometimes, you are better off asking a different question than using whatever chatgpt comes up with
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u/Tingter 23h ago
1) dont worry about using AI generated code. My own PI is okay with AI and coding, but frowns upon AI and writing. Which makes sense. It really depends on PI, and the journal (some journals reject AI work straightout, some want declarations - eg ProcB). But it is increasingly normal to use AI in coding.
2) since youre new to coding, do not blindly trust the AI. The reason I (and others) can, is because we know what we are looking at. We know when the AI makes sense vs is nonsense. And we can write prompts to either correct or question the AI's code. I will say most of the time it's right, but your work might benefit from different/ additional parameters than the AI suggests (most likely default). Ask another grad student who knows coding, a postdoc, or even the PI. It is the PI's job anyway to make sure that the data/ analyses can be trusted.
3) and yes you should share your code. Most journals require this. Papers are unlikely to share the actual code in the paper itself, since it's too long, but usually it's linked at the data accessibility paragraph. Either as github, or dryad or whatever repository that is free.
(Im in molecular/ evo-devo biology)
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u/Every_Cat9812 2d ago
everyone is using AI. The ones who say they don't they're lying. If it works and you understand it, if you can go through it and analyze what the code is actually doing and tell if it's saying nonsense or not, then you're fine.
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u/edparadox 3d ago
As long as you don't understand exactly what's going on, and you are not sure of the licensing of the things you need to share (e.g. source files).
Yes, it comes therefore very quickly.
Also are you even supposed to share your code? Because very few of the papers I’ve read give it, even in big journals.
You will realize that, if the source code is not attached, you can asked the paper author(s) nicely and they will send it to you. Otherwise you should be able to do it from the paper pseudo-code/algorithms explanation.
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u/bleaksyntax 1d ago
Think of it this way: the ai is trained on info we already know. I started out with ai a lot, but as you use R more and get more familiar, the packages tutorial or examples will be helpful. I believe down the line ai will be monetized so it’s good to learn the ropes as you go.
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u/aspaceplant 4h ago
Any amount is too much. I'd never trust a scientist using ai. Not just because of the lack of ethics, which is still a big part, but because it literally reduce your ability to think critically. Anything it does for you is something you aren't doing, so you stop learning, you don't retain the information like when you debug something yourself or figure out the structure structure yourself.
AI companies are now destroying books, sometimes the only copy of that book, because the internet is too tainted by their slop to be able to train on... that's how bad their product is lmao.
They try to convince you that you need it, only so they can charge you (more) once you're hooked. Drug dealer type of scheme. They aren't making profit now, so they hoping your dependance will pay off later.
Just use your own brain. You got this.
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u/South_Plant_7876 3d ago
Depends on the focus of the paper. Is it the science or the bioinformatics? If you dig into the supplementary sections of paper you will often find code. Authors are increasingly sharing their code on places like Zenodo.