semanticscholar has been amazing, and I feel like I am often recommending new papers to people who haven’t encountered them yet thanks to its feeds; the way you use them is by adding a paper to your library, which requires an account, but it only takes a few papers before you start getting ai recommendations. if you try just one, it’s my recommendation. I’ve tried a few paper navigation tools, and my favorite so far is actually manually walking the citation graph on semanticscholar, followed by browsing its new-papers feeds.
I also have been absolutely blown away by metaphor. I’d definitely recommend trying metaphor for your paper search. it can’t do everything but it provides an incredible component and is probably the most general tool I’ve recommended here.
if you find semanticscholar and metaphor disappointing is when I’d suggest you start trying a bunch of these tools in quick succession; set a goal of a kind of discovery you’ve had before that you’d like to have again, and see if the tool can replicate it. There are a lot of really cool papers, and that’s how I find the coolest crazy-advanced-bio-whatever stuff so far; metaphor might be going to replace semanticscholar but ultimately neither are as strong as iris or causaly, afaict.
that said—I suspect that the most advanced bio tool on this list is advanced enough to make a night-and-day difference in your research throughput, and that opening all the bio links and setting a ten minute timer to close all but three would really give you some solid candidates. if you describe what you’re looking for further, I can try filtering further.
https://het.io/explore/ my score: ++++++ this looks very cool! doesn’t look to be modern deep learning but rather a fairly dense plain knowledge graph
Do you know of any AI tools where I can input a table of labeled genetic data and get out an interesting hypothesis? If nothing like that exists, I should probably make one myself.
initial metaphor.systems query by just copying your comment didn’t do much of use for me.
also tried browsing semanticscholar from scratch using semanticscholar search, and didn’t find anything even close.
also checked my saved papers and followups to see if I had favorited anything near that—nope.
from memory, the openbioml folks have been talking about bio language models. there’s been work on text to genome or genome to text. perhaps something in that domain.
some mildly interesting results from tossing the first promising result, genoml, into m.s as a similarity search. manually filtered vaguely-cool-lookin results of projects—these seem like low quality results to me, but I’m not sure:
semanticscholar has been amazing, and I feel like I am often recommending new papers to people who haven’t encountered them yet thanks to its feeds; the way you use them is by adding a paper to your library, which requires an account, but it only takes a few papers before you start getting ai recommendations. if you try just one, it’s my recommendation. I’ve tried a few paper navigation tools, and my favorite so far is actually manually walking the citation graph on semanticscholar, followed by browsing its new-papers feeds.
I also have been absolutely blown away by metaphor. I’d definitely recommend trying metaphor for your paper search. it can’t do everything but it provides an incredible component and is probably the most general tool I’ve recommended here.
if you find semanticscholar and metaphor disappointing is when I’d suggest you start trying a bunch of these tools in quick succession; set a goal of a kind of discovery you’ve had before that you’d like to have again, and see if the tool can replicate it. There are a lot of really cool papers, and that’s how I find the coolest crazy-advanced-bio-whatever stuff so far; metaphor might be going to replace semanticscholar but ultimately neither are as strong as iris or causaly, afaict.
that said—I suspect that the most advanced bio tool on this list is advanced enough to make a night-and-day difference in your research throughput, and that opening all the bio links and setting a ten minute timer to close all but three would really give you some solid candidates. if you describe what you’re looking for further, I can try filtering further.
also, for baseline, I tossed your comment into metaphor with some prompt engineering; here are the results: (<icon loadingspinner/>, manually...)
foss or freeware:
https://het.io/explore/ my score: ++++++ this looks very cool! doesn’t look to be modern deep learning but rather a fairly dense plain knowledge graph
https://biokeanos.com/search my score: ++
https://scite.ai/ my score: ++ seems like a maybe interesting addition to semanticscholar but not that cool on its own
https://pharos.nih.gov/ my score: +
https://www.bioz.com/ my score: +
$+:
https://iris.ai/ my score: ++++++ looks very very cool but kinda expensive
https://academicsequitur.com/ my score: + seems like a crappy semantic scholar to me
$$$ (no price given)+:
https://www.causaly.com/ my score: ++++++++ maybe the coolest one on this list but no price given, probably a LOT
https://epistemic.ai/ my score: +++
https://www.biorelate.com/ my score: ++
https://abzu.ai/ my score: + - they have target identification
https://www.pharm.ai/ (protein binding estimate and such) my score: +
research lab focused on the topic of bio hypothesis discovery: https://discoverylab.ai/
not available yet but whoa cool:
https://www.ideaflow.io/
https://allchemy.net/
https://www.asimov.com/ ← this is probably the most ambitious project on here, though you can’t use it right now
https://www.springdiscovery.com/technology
https://www.medra.ai/
wat, collective behavior aggregation thing but I’m not sure if it’s good or not, or, what:
https://start.polyplexus.com/ my score: ++++
https://unanimous.ai/swarm/ real time voting system for incrementalized communication? seems like it could be prone to group bias tho
misc foss tools that were not what you seek, unrelated but cool:
https://genoml.com/
https://deepchem.io/
https://torchdrug.ai/
https://www.h1st.ai/tutorials huh interesting transparency tool
https://www.omigami.com/
https://www.knime.com/
https://www.notably.ai/
Do you know of any AI tools where I can input a table of labeled genetic data and get out an interesting hypothesis? If nothing like that exists, I should probably make one myself.
I don’t know of one. Here’s what I found looking on semanticscholar and metaphor for
ten minutesan hour or two of diffuse-focus multitasking:near misses:
tool you could use to build it: https://genoml.com/
tool you could use to build it (contains pretrained models of questionable but possible relevance) https://torchdrug.ai/
pretrained models for genomics http://kipoi.org/about/
personalized clinical phenotyping review paper, cited by some interesting looking papers, cites some interesting looking papers. may be a useful node on the research graph, you’ll want to spider manually from here https://www.semanticscholar.org/paper/Personalized-Clinical-Phenotyping-through-Systems-Cesario-D’Oria/99aa941bb47243777731c92e3583b4e78953938b
another review of epigenetics-ml studies https://www.semanticscholar.org/paper/Artificial-Intelligence-in-Epigenetic-Studies%3A-on-Brasil-Neves/7a4589ab51c9248d05567255dad3c2acd927a27c
search trace:
initial metaphor.systems query by just copying your comment didn’t do much of use for me.
also tried browsing semanticscholar from scratch using semanticscholar search, and didn’t find anything even close.
also checked my saved papers and followups to see if I had favorited anything near that—nope.
from memory, the openbioml folks have been talking about bio language models. there’s been work on text to genome or genome to text. perhaps something in that domain.
some mildly interesting results from tossing the first promising result, genoml, into m.s as a similarity search. manually filtered vaguely-cool-lookin results of projects—these seem like low quality results to me, but I’m not sure:
vaporware/sign up for access/expensive/dead startup/other misses: http://20n.com/ https://tracked.bio/ https://www.brainome.ai/ https://www.solvergen.com/
off topic but cool and funky https://www.openml.org/
almost! https://openbioml.org/ (good result, already knew of them, might be relevant to what you seek)
almost! https://torchdrug.ai/
almost! http://kipoi.org/about/
so, okay, let’s try “deep learning on genomics data to do causal discovery of hypotheses for protein and disease function and etiology” (search link):
vaporware/sign up for access/expensive/dead startup/other misses https://latentsci.com/
miss but interesting https://qdata.github.io/qdata-page/categories/AIbiomed/
miss, https://pubmed.ncbi.nlm.nih.gov/33205126/
but through it I found a paper citing it: https://www.semanticscholar.org/paper/Artificial-Intelligence-in-the-Healthcare-System%3A-Lorkowski-Grzegorowska/b97af50b0e117b272785e55a8b83067c296dfbd5
and its cited papers are interesting
still seems like https://www.causaly.com/ is your best paid option
tried semanticscholar search for “artificial intelligence in healthcare”, found some interesting results
https://www.semanticscholar.org/paper/Artificial-Intelligence-in-Medicine-Coiera/e94fa9bebdfd08c13785385be3975e23dc2756ca?sort=is-influential
hmm what about doing a relatedness semantic search on metaphor with a “but for genomics” prompt. ooh interesting results here
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6373233/
found via “artificial intelligence genomics” on semanticscholar
interesting neurosymbolic hybrid thing for explainable genomics something or other: https://www.semanticscholar.org/paper/Artificial-Intelligence-in-Biological-Modelling-Fages/f1e286a353c1b628e8613b940a222525fa6c59d8
cited by this, a very interesting lookin abstract about modern gofai for dynamical systems in biology and explainable deep learning https://www.semanticscholar.org/paper/Learning-any-memory-less-discrete-semantics-for-by-Ribeiro-Folschette/209288da6076e5a828172a4e8ca20c38ba1aeb22
https://www.semanticscholar.org/paper/Integrated-Analysis-of-Whole-Genome-and-Epigenome-Asada-Kaneko/0167c214f4ee1442bc747e0fc513479d80b3ce5b
https://www.semanticscholar.org/paper/AI-applications-in-functional-genomics-Caudai-Galizia/dc65f61c5a6609dd3b35e000bdd47dbfaf6e6eb8
https://www.semanticscholar.org/paper/Artificial-Intelligence%2C-Healthcare%2C-Clinical-and-Abdelhalim-Berber/1eb5cd8804467363c7b25e0a72379c7e264c4b53
https://www.semanticscholar.org/paper/Single-Cell-Analysis-Using-Machine-Learning-and-Its-Asada-Takasawa/2bea2c257322761c161ae1819397c24748ca6c42
misc barely-related
misc paper on biodefense using ai https://www.semanticscholar.org/paper/Big-Data-and-Artificial-Intelligence-for-A-Approach-Valdivia-Granda/a26e064d18d49705170b31cc06cd48b21bf005d5
crop breeding with ai https://www.semanticscholar.org/paper/Applications-of-Artificial-Intelligence-in-Breeding-Khan-Wang/3b97987c4b831b0f7533aab4197c594a6de1c9d9
Thanks! I know this is super late, but this has really improved my work productivity. I really appreciate you taking the time to help.
For what it’s worth, Causaly is a disappointment. No strong LLM integration means it really struggles to compete some of the other products out there.