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    Arc's PIE explores using prior biological knowledge for direct differential expression predictions

    An Arc team member says PIE is a spinoff of a program producing AI-biology model candidates every six months.

    Patrick HsuPH
    Rishi VermaRV
    Dave BurkeDB
    4 Sources, ,

    TLDR

    An Arc team member says PIE explores using prior biological knowledge sources to make direct differential expression predictions. They describe it as a useful result as Arc works toward more general cell simulators.

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    4 Sources, first seen 10h ago

    Combined views

    3.3K

    4 Sources, first seen 10h ago

    34 likes
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    first seen 10h ago
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    4 Sources

    Rishi Verma@i_m_riveThis is my first ever post on X! And I’d love to share my first ever preprinted work PIE - Perturbation is Everything! Generalizing across unseen cellular and experimental conditions is challenging but essential to therapeutics discovery. Our latest model PIE addresses this challenge by simplifying the learning objective and leveraging curated biological knowledge from public databases. PIE generalizes across a combinatorial space of perturbation studies, unseen cell lines and unseen perturbations. Check out the preprint at https://doi.org/10.64898/2026.10.02.756297 Huge shoutout to the team at @arcinstitute for driving this project:@abhinadduri , @beabevi_ , @basak_ersln , @davey_burke , @genophoria , @yusufroohani .10h
    Dave Burke@davey_burkeAt Arc we have a compounding delivery cycle for our AI/bio models, with model candidates every 6 months. Sometimes we have spinoffs where we explore ideas and cherry pick ideas forward. PIE is one such example. It explores how far we can push using prior biological knowledge sources to make direct differential expression predictions. Useful result as we build towards more general cell simulators. Congrats @i_m_rive, @genophoria, @yusufroohani and team.4h
    Patrick Hsu@pdhsuRT @davey_burke: At Arc we have a compounding delivery cycle for our AI/bio models, with model candidates every 6 months. Sometimes we have…1h

    4 Sources

    Rishi Verma@i_m_riveThis is my first ever post on X! And I’d love to share my first ever preprinted work PIE - Perturbation is Everything! Generalizing across unseen cellular and experimental conditions is challenging but essential to therapeutics discovery. Our latest model PIE addresses this challenge by simplifying the learning objective and leveraging curated biological knowledge from public databases. PIE generalizes across a combinatorial space of perturbation studies, unseen cell lines and unseen perturbations. Check out the preprint at https://doi.org/10.64898/2026.10.02.756297 Huge shoutout to the team at @arcinstitute for driving this project:@abhinadduri , @beabevi_ , @basak_ersln , @davey_burke , @genophoria , @yusufroohani .10h
    Dave Burke@davey_burkeAt Arc we have a compounding delivery cycle for our AI/bio models, with model candidates every 6 months. Sometimes we have spinoffs where we explore ideas and cherry pick ideas forward. PIE is one such example. It explores how far we can push using prior biological knowledge sources to make direct differential expression predictions. Useful result as we build towards more general cell simulators. Congrats @i_m_rive, @genophoria, @yusufroohani and team.4h
    Patrick Hsu@pdhsuRT @davey_burke: At Arc we have a compounding delivery cycle for our AI/bio models, with model candidates every 6 months. Sometimes we have…1h