Oct 2026· Computer Methods in Biomechanics & Biomedical Engineering· 51 references
EEG and Brain-Computer Interfaces
Abstract
Major Depressive Disorder (MDD) represents a major global health burden, characterized by high recurrence rates and an increased risk of suicide. Traditional clinical diagnoses rely primarily on subjective scales, which limits their objectivity in accurately distinguishing MDD subtypes.To address these challenges, this study introduces a multimodal assessment framework that integrates EEG functional connectivity with clinical scale data.We developed TSCANet (Transformer Local-Global Graph Neural Network Scales Cross-Attention Network), a deep learning multimodal model designed to address the complexity of psychiatric data. TSCANet achieved classification accuracies of 82.62% and 81.36% in visual and auditory paradigms, respectively, outperforming single-modality models.
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...
A. Marchenko, P. Abrahamsson· Agile Conference· 59 citations· ⚡11
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.
Yaser Ghanam, F. Maurer, P. Abrahamsson· Information and Software Tec...· 41 citations· ⚡3
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.
D. Graziotin, Xiaofeng Wang, P. Abrahamsson· Scientometrics· 21 citations· ⚡1
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
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· ⚡1
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...
Nawaf Alampara, Santiago Miret, Kevin Maik Jablonka· Nature Computational Science· 13 citations
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