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AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Scrum in a Multiproject Environment: An Ethnographically-Inspired Case Study on the Adoption Challenges
Agile methods continue to gain popularity. In particular, the Scrum method appears to be on the verge of becoming a de-facto standard in the industry, leading the so called Agile movement. While there are success stories and recommendations, there is little scientifically valid evidence of the challenges in the adoptio...
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Making the leap to a software platform strategy: Issues and challenges
A comprehensive taxonomy of the challenges faced when a medium-scale organization decided to adopt software platforms is provided, namely: business challenges, organizational challenges, technical challenges, and people challenges.
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A framework for systematic analysis of open access journals and its application in software engineering and information systems
It is shown that high article processing charges are not sufficiently justified by the publishers, which often lack transparency and may prevent authors from adopting OA.
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Integration of molecular coarse-grained model into geometric representation learning framework for protein-protein complex property prediction
MCGLPPI, a novel geometric representation learning framework that combines graph neural networks (GNNs) with the MARTINI molecular coarse-grained (CG) model to predict overall PPI properties accurately and efficiently, offers an effective and efficient solution for PPI overall property predictions.
Yang Yue, Shu Li, Yihua Cheng et al.
· bioRxiv · 15 citations
#machine learning
Aug 2025
PepPCBench is a Comprehensive Benchmarking Framework for Protein-Peptide Complex Structure Prediction
PepPCBench enables a robust evaluation of PFNN-based methods and supports their continued development for peptide-protein structure prediction, and highlights the influence of peptide length, conformational flexibility, and training set similarity on prediction accuracy.
Si-Long Zhai, Huifeng Zhao, Ji-Ke Wang et al.
· Journal of Chemical Informat... · 13 citations
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Less can be more for predicting properties with large language models
Abstract Predicting properties from coordinate–category data—sets of vectors paired with categorical information—is fundamental to computational science, manifesting in materials science as predicting formation energies or elastic moduli from crystal structures. While large language models (LLMs) have increasingly been...
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