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GenoRxiv – Mapping scientific literature on the genome

Hacker News

GenoRxiv – Mapping scientific literature on the genome

Ever get lost searching through genetic literature? I've built a new interface for bioRxiv+medRxiv to directly browse preprint findings on the genome! To begin I downloaded 7TB's of bioRxiv+medRxiv preprints and extracted mentions of genetic variant ID's or positions. Documented in "How to download bioRxiv on a budget": http://sitlabs.org/writing/biorxiv.html This project started after I was trawling through Google Scholar to review a gene when I realised I had missed relevant upstream findings which hadn't been linked to the gene. I wondered if there could be a better interface to explore genetic literature? Next I provided the variant mentions and paper context to gpt-4o-mini and requested structured output summarising the scientific finding. Finally I mapped all variants to the latest genome build and plotted them on our custom genome browser. This project was made by the new Scientific Interface & Tooling Lab ( http://sitlabs.org ) at Oxford. We will be releasing new interfaces and tools to improve scientific productivity with an initial focus on biology research.

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, new, context · Missing: mac, agents, macos
86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: interface · Missing: plus, platform, intuitive
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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