Evidence map›Paper›PMID 39398201›Full record

ArticleArXiv2024

How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities.

Charlotte Bunne, Yusuf Roohani, Yanay Rosen, Ankit Gupta, Xikun Zhang, Marcel Roed, Theo Alexandrov, Mohammed AlQuraishi, Patricia Brennan, Daniel B Burkhardt and 32 more

Abstract readPreprint
PubMed Full text at PMC
In one paragraph

Article in ArXiv, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

42 authors.

Charlotte BunneDepartment of Computer Science, Stanford University, Stanford, CA, USA.ORCID 0000-0003-1431-103X
Yusuf RoohaniDepartment of Computer Science, Stanford University, Stanford, CA, USA.ORCID 0000-0002-9577-5014
Yanay RosenDepartment of Computer Science, Stanford University, Stanford, CA, USA.ORCID 0000-0003-1129-9645
Ankit GuptaChan Zuckerberg Initiative, Redwood City, CA, USA.ORCID 0000-0002-9961-1041
Xikun ZhangDepartment of Computer Science, Stanford University, Stanford, CA, USA.ORCID 0000-0002-8346-8594
Marcel RoedDepartment of Computer Science, Stanford University, Stanford, CA, USA.ORCID 0000-0002-1369-8933
Theo AlexandrovDepartment of Pharmacology, University of California, San Diego, CA, USA.
Mohammed AlQuraishiDepartment of Systems Biology, Columbia University, New York, NY, USA.
Patricia BrennanChan Zuckerberg Initiative, Redwood City, CA, USA.
Daniel B BurkhardtCellarity, Somerville, MA, USA.
Andrea CalifanoDepartment of Systems Biology, Columbia University, New York, NY, USA.
Jonah CoolChan Zuckerberg Initiative, Redwood City, CA, USA.
Abby F DernburgDepartment of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA.
Kirsty EwingChan Zuckerberg Initiative, Redwood City, CA, USA.
Emily B FoxDepartment of Computer Science, Stanford University, Stanford, CA, USA.
Matthias HauryChan Zuckerberg Institute for Advanced Biological Imaging, Redwood City, CA, USA.
Amy E HerrChan Zuckerberg Biohub San Francisco, CA, USA.
Eric HorvitzMicrosoft Research, Redmond, WA, USA.
Patrick D HsuArc Institute, Palo Alto, CA, USA.
Viren JainGoogle Research, Mountain View, CA, USA.
Gregory R JohnsonNewLimit, San Francisco, CA, USA.
Thomas KalilSchmidt Futures, USA.
David R KelleyCalico Life Sciences LLC, San Francisco, CA, USA.
Shana O KelleyChan Zuckerberg Biohub Chicago, IL, USA.
Anna KreshukCell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
Tim MitchisonDepartment of Systems Biology, Harvard Medical School, Boston, MA, USA.
Stephani OtteChan Zuckerberg Institute for Advanced Biological Imaging, Redwood City, CA, USA.
Jay ShendureDepartment of Genome Sciences, University of Washington, Seattle, WA, USA.
Nicholas J SofroniewEvolutionaryScale, PBC, USA.
Fabian TheisInstitute of Computational Biology, Helmholtz Center Munich, Munich, Germany.
Christina V TheodorisGladstone Institute of Cardiovascular Disease, Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.
Srigokul UpadhyayulaDepartment of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA.
Marc ValerChan Zuckerberg Initiative, Redwood City, CA, USA.
Bo WangDepartment of Computer Science, University of Toronto, Toronto, Ontario, Canada.
Eric XingCarnegie Mellon University, School of Computer Science, Pittsburgh, PA, USA.
Serena Yeung-LevyDepartment of Computer Science, Stanford University, Stanford, CA, USA.
Marinka ZitnikDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Theofanis KaraletsosChan Zuckerberg Initiative, Redwood City, CA, USA.ORCID 0000-0002-0296-3092
Aviv RegevGenentech, South San Francisco, CA, USA.ORCID 0000-0003-3293-3158
Emma LundbergChan Zuckerberg Initiative, Redwood City, CA, USA.ORCID 0000-0001-7034-0850
Jure LeskovecDepartment of Computer Science, Stanford University, Stanford, CA, USA.ORCID 0000-0002-5411-923X
Stephen R QuakeChan Zuckerberg Initiative, Redwood City, CA, USA.ORCID 0000-0002-1613-0809

Funding

Bridge2AI: Cell Maps for AI (CM4AI) Data Generation ProjectOT2OD032742 · OD · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Jean-Christophe Bélisle-Pipon, TIMOTHY W CLARK · 2022 to 2026
$21.5M
The Cancer Cell Map Initiative v2.0U54CA274502 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Trey Ideker · 2022 to 2026
$14.2M
NCI NIH HHS U54 CA274502NIH HHS OT2 OD032742
6 · The paper itself

Abstract

The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intelligence (AI), combined with the ability to generate large-scale experimental data, present novel opportunities to model cells. Here we propose a vision of leveraging advances in AI to construct virtual cells, high-fidelity simulations of cells and cellular systems under different conditions that are directly learned from biological data across measurements and scales. We discuss desired capabilities of such AI Virtual Cells, including generating universal representations of biological entities across scales, and facilitating interpretable

Identifiers

PMID39398201
PMCPMC11468656

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.