Evidence map›Paper›PMID 40880880›Full record

ArticleFrontiers in artificial intelligence2025

BioBricks.ai: a versioned data registry for life sciences data assets.

Yifan Gao, Zakariyya Mughal, Jose A Jaramillo-Villegas, Marie Corradi, Alexandre Borrel, Ben Lieberman, Suliman Sharif, John Shaffer, Karamarie Fecho, Ajay Chatrath and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. AI snake oil? A risk/benefit analysis for toxicology.Frontiers in artificial intelligence · 2026
    Article
  5. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors.

Yifan GaoCenter for Alternative to Animal Testing, Johns Hopkins University, Baltimore, MD, United States.
Zakariyya MughalInsilica, Bethesda, MD, United States.
Jose A Jaramillo-VillegasLaboratory for Research in Complex Systems, Menlo Park, CA, United States.
Marie CorradiHU University of Applied Sciences Utrecht, Innovative Testing in Life Sciences and Chemistry, University of Applied Sciences Utrecht, Utrecht, Netherlands.
Alexandre BorrelInotiv, Research Triangle Park, NC, United States.
Ben LiebermanInsilica, Bethesda, MD, United States.
Suliman SharifInsilica, Bethesda, MD, United States.
John ShafferInsilica, Bethesda, MD, United States.
Karamarie FechoRenaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
Ajay ChatrathDepartment of Neurological Surgery, Washington University in Saint Louis, St. Louis, MO, United States.
Alexandra MaertensCenter for Alternative to Animal Testing, Johns Hopkins University, Baltimore, MD, United States.
Marc A T TeunisHU University of Applied Sciences Utrecht, Innovative Testing in Life Sciences and Chemistry, University of Applied Sciences Utrecht, Utrecht, Netherlands.
Nicole KleinstreuerNTP Interagency Center for the Evaluation of Alternative Methods, Research Triangle Park, NC, United States.
Thomas HartungCenter for Alternative to Animal Testing, Johns Hopkins University, Baltimore, MD, United States.
Thomas LuechtefeldCenter for Alternative to Animal Testing, Johns Hopkins University, Baltimore, MD, United States.

Funding

BioBricks-Env: AI driven, open-source, modular, composable platform to organize, store, retrieve, extract, and integrate environmental health & risk related dataR43ES036069 · NIEHS · INSILICA, LLC · PI LUECHTEFELD, THOMAS · 2024 to 2024
$296k
ToxIndex-CPG: Machine learning driven platform integrating a hazard susceptibility database to quantify chemical toxicity factors, predict risk levels and classify biological responsesR43ES033851 · NIEHS · INSILICA, LLC · PI LUECHTEFELD, THOMAS · 2022 to 2022
$256k
NIEHS NIH HHS R43 ES033851NIEHS NIH HHS R43 ES036069
6 · The paper itself

Abstract

Introduction: Researchers in biomedicine and public health often spend weeks locating, cleansing, and integrating data from disparate sources before analysis can begin. This redundancy slows discovery and leads to inconsistent pipelines. Methods: We created BioBricks.ai, an open, centralized repository that packages public biological and chemical datasets as modular "bricks." Each brick is a Data Version Control (DVC) Git repository containing an extract‑transform‑load (ETL) pipeline. A package‑manager-like interface handles installation, dependency resolution, and updates, while data are delivered through a unified backend (https://biobricks.ai). Results: The current release provides >90 curated datasets spanning genomics, proteomics, cheminformatics, and epidemiology. Bricks can be combined programmatically to build composite resources; benchmark use‑cases show that assembling multi‑dataset analytic cohorts is reduced from days to minutes compared with bespoke scripts. Discussion: BioBricks.ai accelerates data access, promotes reproducible workflows, and lowers the barrier for integrating heterogeneous public datasets. By treating data as version‑controlled software, the platform encourages community contributions and reduces redundant engineering effort. Continued expansion of brick coverage and automated provenance tracking will further enhance FAIR (Findable, Accessible, Interoperable, Reusable) data practices across the life‑science community.

Indexed as

BioBricks.aibioinformaticscheminformaticsdata integrationmachine learningpublic health data

Identifiers

PMID40880880
PMCPMC12380851

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.