Cinco de Bio: A Low-Code Platform for Domain-Specific Workflows for Biomedical Imaging Research

Abstract

Background: In biomedical imaging research, experimental biologists generate vast amounts of data that require advanced computational analysis. Breakthroughs in experimental techniques, such as multiplex immunofluorescence tissue imaging, enable detailed proteomic analysis, but most biomedical researchers lack the programming and Artificial Intelligence (AI) expertise to leverage these innovations effectively. Methods: Cinco de Bio (CdB) is a web-based, collaborative low-code/no-code modelling and execution platform designed to address this challenge. It is designed along Model-Driven Development (MDD) and Service-Orientated Architecture (SOA) to enable modularity and scalability, and it is underpinned by formal methods to ensure correctness. The pre-processing of immunofluorescence images illustrates the ease of use and ease of modelling with CdB in comparison with the current, mostly manual, approaches. Results: CdB simplifies the deployment of data processing services that may use heterogeneous technologies. User-designed models support both a collaborative and user-centred design for biologists. Domain-Specific Languages for the Application domain (A-DSLs) are supported through data and process ontologies/taxonomies. They allow biologists to effectively model workflows in the terminology of their field. Conclusions: Comparative analysis of similar platforms in the literature illustrates the superiority of CdB along a number of comparison dimensions. We are expanding the platform’s capabilities and applying it to other domains of biomedical research.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 13 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.