The scientometric approach to Code Biology: What the title tells about the field.

Abstract

Code Biology has emerged as a conceptual framework for investigating how information is encoded, transmitted, and interpreted in living systems. Building on recent efforts to catalog biological codes across disciplines, we present the first comprehensive scientometric analysis of the field. Using a curated corpus of publications explicitly invoking the term code, we apply full-text natural language processing and unsupervised topic modeling to map the intellectual landscape of Code Biology. Our analysis reveals 24 distinct thematic clusters, ranging from molecular mechanisms and regulatory architectures to neural information processing and philosophical discourse on meaning and organization. This approach offers insights that conventional literature reviews often miss-uncovering latent patterns, inter-topic correlations, and conceptual blind spots. In doing so, we expose the field's current fragmentation into isolated knowledge niches and highlight the need for integrative models of how biological codes interact across scales. Temporal and geographical analyses reveal distinct phases in the development of Code Biology, shifting from gene-centric and mechanistic views to increasingly symbolic, cognitive, and systems-oriented paradigms. Collaboration network analysis further shows the emergence of modular scientific communities and identifies key interdisciplinary contributors shaping the field. Taken together, our results establish the foundation for a new branch of code biology, dedicated to the empirical and conceptual mapping of coding processes in biology based on literature. We propose key research directions, including the structural grammar of neural codes, the role of prebiotic and evolutionary codes in transitions of life, and the intersection between biological and artificial coding systems. This work provides not only a roadmap for future research but also a call to develop standardized frameworks capable of bridging molecular, neural, and symbolic dimensions of biological information processing.