Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
This paper explores the development of novel algorithmic complexity metrics specifically tailored for analyzing the computational demands of biological systems, including protein folding, gene regulation, and cellular signaling pathways. Traditional complexity measures often lack the nuanced understanding required to effectively model and analyze these systems. This work proposes a new set of metrics focusing on both the quantity and efficiency of operations, aiming to provide a more robust and insightful framework for understanding the computational bottlenecks inherent in biological processes. We introduce a new metric, the "Operational Efficiency Score" (OES), which incorporates both computational cost and the degree of resource utilization, offering a more comprehensive assessment of algorithmic complexity. The paper details the design and validation of the OES, emphasizing its potential to improve our understanding of biological computational challenges and guide the development of more efficient algorithms.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
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This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
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This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.
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This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.
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It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or sequence constraints.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
