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The Hedgehog gene (Hh) was first discovered due to its control of the growth of disorganized, hair-like bristles phenotype in Drosophila, much like hedgehog spines. Hh plays a crucial role in the development of organs and the maintenance of homeostasis in both invertebrates and vertebrates. However, while Drosophila has only one Hh protein, mammals have multiple functional Hedgehog proteins - Sonic (Shh), Desert (Dhh), and Indian Hedgehog (Ihh). All of these homologous proteins have adapted to...
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Updated: Sep 15, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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Heron, a Knowledge Graph editor for intuitive implementation of Python-based experimental pipelines.

George Dimitriadis1, Ella Svahn1,2, Andrew F MacAskill2

  • 1Sainsbury Wellcome Centre, University College London, London, United Kingdom.

Elife
|July 16, 2025
PubMed
Summary
This summary is machine-generated.

Heron is a Python-based platform that simplifies building and running experimental pipelines. It enhances transparency and reproducibility by using Knowledge Graphs, making complex research setups more accessible.

Keywords:
analysisexperimental controlexperimental setupmouseneuroscienceratsoftware

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Area of Science:

  • Experimental sciences, including robotics, neuroscience, behavioral sciences, physics, chemistry, and environmental science.

Background:

  • Researchers face trade-offs between implementation ease and experimental flexibility/reproducibility.
  • Integrating complex hardware and software combinations presents significant challenges.

Purpose of the Study:

  • To introduce Heron, a Python-based platform for constructing and executing experimental and data analysis pipelines.
  • To enhance transparency, reproducibility, and accessibility in experimental research.

Main Methods:

  • Heron utilizes a Knowledge Graph to represent experimental design, mirroring the logical workflow.
  • The platform integrates hardware and software, offering a balance between control and ease of use.
  • It combines high-level programming with Graphical User Interfaces, requiring intermediate Python proficiency.

Main Results:

  • Heron accelerates implementation and updates while minimizing opaque components.
  • It facilitates the integration of complex, previously costly, hardware-software combinations.
  • The platform empowers researchers without formal engineering backgrounds to build reliable, reproducible experimental setups.

Conclusions:

  • Heron bridges the gap between scientific creativity and technical implementation.
  • It promotes a modular codebase, encouraging documentation and reuse.
  • The platform enhances the transparency and reproducibility of experimental research.