Draft:GenBio AI
Submission declined on 19 December 2024 by WeirdNAnnoyed (talk).
Where to get help
How to improve a draft
You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Editor resources
|
- Comment: The subject does not appear to be notable by the guidelines of WP:NCORP. All sources cited are press releases, the company's own material, or LinkedIn or other unreliable self-published sources. We need significant coverage in more than one reliable, independent source to have an article. WeirdNAnnoyed (talk) 21:49, 19 December 2024 (UTC)
This is a draft article. It is a work in progress open to editing by anyone. Please ensure core content policies are met before publishing it as a live Wikipedia article. Find sources: Google (books · news · scholar · free images · WP refs) · FENS · JSTOR · TWL Last edited by WeirdNAnnoyed (talk | contribs) 3 seconds ago. (Update)
Finished drafting? or |
Company type | Private |
---|---|
Industry | |
Founded | 2024 |
Founders |
|
Headquarters | |
Key people |
|
Website | genbio |
GenBio AI (legal name: GenBio.AI, Inc.[1]) is a biotechnology and artificial intelligence company headquartered in Palo Alto, California, with satellite offices in Paris and Abu Dhabi. The company focuses on the development of the world’s first AI-Driven Digital Organism (AIDO), an integrated system of multiscale foundation models designed to simulate, program, and predict biological outcomes at various scales, including DNA, RNA, proteins, cells, and evolutionary data. [2][3][4]
History
[edit]GenBio AI was founded in 2024 by Eric Xing and Le Song, prominent researchers in machine learning and computational biology. The company’s launch coincided with the presentation of six peer-reviewed technical papers at the Conference on Neural Information Processing Systems (NeurIPS)[5] detailing the technical framework behind AIDO. [6]
Technology
[edit]GenBio AI’s flagship technology is the AI-Driven Digital Organism (AIDO), which integrates six foundational models that span multiple levels of biological complexity:
- AIDO-DNA: A 7-billion-parameter model trained on genomic data from 796 species, designed for genomic function and structure analysis.[7]
- AIDO-RNA: A 1.6-billion-parameter model focused on RNA structure prediction, genetic regulation, and vaccine development.
- AIDO-Protein: A computationally efficient model for studying protein functions and interactions.
- AIDO-Single Cell: Pre-trained on a dataset of 50 million human cells, capable of analyzing entire transcriptomes.
- Protein Structure Model: Specializing in three-dimensional modeling of protein folding and structure-function relationships.
- Evolutionary Information Model: Providing insights into molecular evolution.
The models are interoperable, enabling a unified platform for simulating and programming biological processes across molecular, cellular, and systemic levels. AIDO is noted for its multitasking efficiency, capable of solving up to 300 tasks simultaneously.[8]
Applications
[edit]GenBio AI’s technology addresses critical challenges in medicine and biotechnology:
- Drug Discovery: AIDO accelerates the identification of potential therapeutics by simulating millions of compounds and predicting their biological effects.
- Personalized Medicine: The platform supports the creation of digital patient twins to design customized treatment plans and reduce adverse drug reactions.
- Disease Understanding: AIDO provides tools to study systemic interactions, enabling researchers to explore conditions such as cancer and neurodegenerative diseases.
Research Contributions
[edit]The company has published six technical papers outlining the methodologies behind AIDO. These include advancements in sparse transformers, retrieval-augmented learning, and large-scale biological data integration. The research establishes AIDO as a new standard in biological modeling.
Leadership
[edit]- Eric Xing: Co-founder and Chief Scientist, a pioneer in AI and computational biology.[9]
- Le Song: Co-founder and Chief Technology Officer, specializing in AI applications in biological systems.
The advisory board includes prominent scientists such as:
- Eran Segal: Department of Computer Science, Weizmann Institute of Science.
- Fabian Theis: Director of the Institute for Computational Biology at Helmholtz Munich.
Global Presence
[edit]GenBio AI operates globally with its headquarters in Palo Alto, and additional labs in Paris and Abu Dhabi. The team includes experts from institutions such as Carnegie Mellon University, Stanford University, MBZUAI, and the Weizmann Institute of Science.[10]
External Links
[edit]See also
[edit]References
[edit]- ^ "GenBio AI Legal Name". GenBio AI.
- ^ "GenBio AI Releases Phase 1 of World's First Digital Organism to Transform Medical Research". Yahoo Finance.
- ^ "GenBio AI Releases Phase 1 of World's First Digital Organism to Transform Medical Research". Associated Press. 19 December 2024.
- ^ "GenBio AI Releases Phase 1 of World's First Digital Organism to Transform Medical Research". BioSpace. 19 December 2024.
- ^ "AI for New Drug Modalities". AIDrugX at NeurIPS 2024.
- ^ "GenBio AI - Published Research at NeurIPS 2024". GenBio AI.
- ^ "Toward AI-Driven Digital Organism: A System of Multiscale Foundation Models for Predicting, Simulating and Programming Biology at All Levels". arXiv. 9 December 2024.
- ^ "GenBio AI - Published Research at NeurIPS 2024". GenBio AI.
- ^ Xing, Eric. "Professor Eric Xing announces GenBio AI to the public on LinkedIn". LinkedIn.
- ^ "Global Offices, GenBio AI". GenBio AI.
- in-depth (not just brief mentions about the subject or routine announcements)
- reliable
- secondary
- strictly independent of the subject
Make sure you add references that meet all four of these criteria before resubmitting. Learn about mistakes to avoid when addressing this issue. If no additional references exist, the subject is not suitable for Wikipedia.