https://github.com/paritoshk/antiaging_app

Are you curious about whether we are close to unlocking secrets of immortality? Where is science going about anti-aging? Don't have time to read 3000+ research papers published by startups with over $5B in funding? Are you a individual investor, and interested in seeing due diligence on these startups?
https://github.com/paritoshk/antiaging_app

Last synced: 10 months ago
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Are you curious about whether we are close to unlocking secrets of immortality? Where is science going about anti-aging? Don't have time to read 3000+ research papers published by startups with over $5B in funding? Are you a individual investor, and interested in seeing due diligence on these startups?

• Host: GitHub
• URL: https://github.com/paritoshk/antiaging_app
• Owner: paritoshk
• License: gpl-3.0
• Created: 2023-01-01T19:26:52.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2025-03-21T22:53:24.000Z (10 months ago)
• Last Synced: 2025-03-21T23:29:25.997Z (10 months ago)
• Language: Jupyter Notebook
• Size: 23.4 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# Longevity AI

### A conversation AI that returns critical information about the research area  

### Semantic search across the vast database of pre-selected companies leading in the space

### Tools - Streamlit, Hugging Face Sentence Xformers, and Pandas

https://longevityai.streamlit.app/

### LongevityApp QR Code Scan below: 



Are you curious about whether we are close to unlocking the secrets of immortality? Where is science going about anti-aging? Need more time to read 3000+ research papers published by startups with over $10 Billion in funding? Are you an individual investor and interested in seeing due diligence on these startups? 

You have landed on the right spot. This application is a semantic search engine that attempts to automate steps of the competitive landscape and topic modeling and builds a mental knowledge graph that sparks thoughts, discussions, and decisions.

## More info

![1](https://user-images.githubusercontent.com/9400939/215384283-a4eab303-d204-4857-afb0-67c2a9a521f3.png)

![2](https://user-images.githubusercontent.com/9400939/215384287-45c1abb5-855f-45e7-8533-46c4d02e4c51.png)

![3](https://user-images.githubusercontent.com/9400939/215384289-96875b65-5691-4d32-96ce-465515f3bd63.png)

![4](https://user-images.githubusercontent.com/9400939/215384292-cecd6f9c-2636-457d-b400-5d7c8dcd1676.png)

![5](https://user-images.githubusercontent.com/9400939/215384295-267aa649-d976-454c-bacd-923f0bbc38ee.png)

![6](https://user-images.githubusercontent.com/9400939/215384297-df7dfd86-f93f-4935-9140-7e9550d1d7c2.png)

![7](https://user-images.githubusercontent.com/9400939/215384298-aec3f685-c427-434b-b276-f77bf714fc67.png)

=======

Refer:

https://towardsdatascience.com/arxiv-scientific-papers-vector-similarity-search-15059970a73a

https://towardsdatascience.com/topic-modeling-with-lsa-plsa-lda-nmf-bertopic-top2vec-a-comparison-5e6ce4b1e4a5