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Bioptimus

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Foundation models that learn multimodal multi-scale human biology to support drug discovery, diagnostics, and clinical trials.

ウェブサイト:https://www.bioptimus.com/
追加日:2026年7月13日
月間訪問者数:
19.0K
ソーシャルと電子メール:
foundation modelsmultimodal AIhuman biologydrug discoverydiagnosticsclinical trials
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Bioptimus
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Bioptimusとは何ですか?

Bioptimus trains native foundation models across biological modalities and scales to learn human biology dynamics and predict outcomes for research, trials, and patient care. Its work centers on building models that understand biology from cell to tissue to organ levels to advance drug discovery, diagnostics, and clinical applications.

コア機能

  • H-Optimus

    Histology foundation model that ranks first in independent pathology benchmarks and extracts spatial biology signals from routine H&E slides.

  • M-Optimus

    Multimodal foundation model that integrates pathology, spatial transcriptomics, and genomics in one architecture to decode biology across scales.

  • STELA data engine

    Multi-institutional initiative that generates deeply profiled, clinically linked multimodal patient data spanning geographies and diseases.

  • Multimodal multi-scale architecture

    Models designed from day one to jointly learn from cell to tissue to organ scales and reveal cross-modal patterns invisible in siloed analysis.

使用例

  • #1

    Drug target and biomarker discovery

    Predict spatial distribution of ADC drug targets, immune microenvironment, and stromal architecture from routine histology slides without new assays.

  • #2

    Indication expansion

    Score archival cohorts across disease, stage, and mechanism of action using archived H&E slides to identify patient subsets suited to a drug.

  • #3

    Diagnostics and spatial biology

    Integrate histology, spatial transcriptomics, and genomics signals in a single pipeline for clinically actionable biology beyond standard workups.

  • #4

    Treatment response and trial design

    Discover multimodal biomarker signatures that distinguish responders from non-responders even in small early-phase cohorts to improve trial success probability.

以下に最適:Pharmaceutical companiesClinical research institutionsAcademic researchers in biology and machine learningHealthcare organizations working with pathology and genomics data

料金プラン

Model access is offered via request on the website; no public pricing tiers or commercial terms are stated on the official homepage.

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よくある質問

What is Bioptimus?
Bioptimus develops foundation models that learn multimodal multi-scale human biology to support drug discovery, diagnostics, and clinical trials. It trains models natively across biological modalities and scales to understand human biology dynamics and predict outcomes for research, trials, and patient care.
What are the main foundation models from Bioptimus?
Key models include H-Optimus, a histology foundation model that ranks first in independent pathology benchmarks and extracts spatial biology signals from routine H&E slides, and M-Optimus, a multimodal model that integrates pathology, spatial transcriptomics, and genomics to decode biology across scales.
What is the STELA data engine?
STELA is a multi-institutional initiative that generates deeply profiled, clinically linked multimodal patient data spanning geographies and diseases.
Who can benefit from Bioptimus?
Pharmaceutical companies, clinical research institutions, academic researchers in biology and machine learning, and healthcare organizations working with pathology and genomics data.
How can I access Bioptimus models or learn about pricing?
Model access is offered via request on the website. No public pricing tiers or commercial terms are stated on the official homepage.
What kinds of problems can Bioptimus help solve?
Use cases include predicting spatial distribution of drug targets from histology slides for biomarker discovery, scoring archival cohorts for indication expansion, integrating multimodal signals for diagnostics and spatial biology, and discovering biomarker signatures to improve treatment response prediction and trial design.

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タグ
foundation modelsmultimodal AIhuman biologydrug discoverydiagnosticsclinical trials
言語英語
最終更新日2026年7月16日
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