Life science laboratories often generate knowledge through iterative experimental adaptation, not fixed workflows. In 3D histology, tissue clearing, and volumetric imaging collaborations, a sample may arrive with unfamiliar properties, requiring researchers to start from a related protocol, decompose it into reusable modules, substitute or remove steps, and adapt choices based on observations, interpretations, and scientific objectives.
In this session, Shi Lin and Hsin Chen will introduce BioGraph, a Neo4j-backed platform for capturing how experimental knowledge is generated, propagated, and reused across collaborations. He will show how protocols can be modeled as dynamic compositions of modules, and how observations, interpretations, decisions, sample characteristics, imaging results, and protocol changes can be linked in an ontological structure. Rather than treating data lineage as a static record, BioGraph will demonstrate how graph-native systems can accumulate module-level evidence about what worked, what failed, under which conditions, and why a later choice was made.
You will learn how to model modular experimental workflows in Neo4j; how to represent the chain from Observation → Interpretation → Decision → New Protocol; and how human-in-the-loop ETL can convert scientific documents, metadata, and experimental results into reviewable graph candidates. Examples from tissue clearing and volumetric imaging will show how knowledge from one collaboration can inform the next.
Speaker
Hsin Chen
Computational Microscopy Specialist, Academia Sinica
Hsin Chen is a 3D Bioimage Analysis Specialist at Academia Sinica who builds scalable computational infrastructure for large, complex scientific datasets. She is the creator of Gradus, a "knowledge-decompression" engine that unfolds dense free-text documents into grounded, prerequisite-ordered maps of understanding. In the Gradus × BioGraph collaboration, her work decomposes complex experimental protocols into modular, traceable relationships and lands them on a Neo4j knowledge graph — so scientific reasoning can be verified against its sources, de-duplicated, and reused across labs.
