I

I built a biological network visualization tool

Hacker News

I built a biological network visualization tool

I've been working on nodes.bio - an interactive tool for visualizing biological networks and systems thinking. The tool features interactive network visualization powered by Cytoscape.js, with real-time graph editing and manipulation capabilities. It supports JSON import/export and provides a responsive design that works seamlessly on the desktop (mobile-friendly version coming later). The tech stack combines modern frontend technologies with robust backend architecture. The frontend uses Next.js 14 with TypeScript and Cytoscape.js for the visualization engine. The backend is built with FastAPI and Python. The featured demo showcases a Traumatic Brain Injury Nasal Spray mechanism of action visualization, demonstrating the tool's capability to handle complex biological pathway mapping. You can explore the live demo at < https://nodes.bio > to see the TBI Nasal Spray visualization in action, along with other biological network examples. I'd love feedback on the visualization capabilities or any suggestions for biological data integration. What do you think?

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
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Product HuntOn track for Day 1 leaderboard · Strong signals: visual · Missing: mac, agents, macos
61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: friendly · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
8%8% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
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0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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