SGTCDA: Prediction of circRNA-drug sensitivity associations with interpretable graph transformers and effective assessment by Hongwei Xia, Caiyue Dong, Xinxing Chen, Zhuoyu Wei, Lichuan Gu, Xiaolei Zhu

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A self-conformation-aware pre-training framework for molecular property prediction with substructure interpretability by Jianbo Qiao, Junru Jin, Ding Wang, Saisai Teng, Junyu Zhang, Xuetong Yang, Yuhang Liu, Yu Wang, Lizhen Cui, Quan Zou, Ran Su, Leyi Wei

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Machine learning based shear strength prediction in reinforced concrete beams using Levy flight enhanced decision trees by Aybike Özyüksel Çiftçioğlu, Anıl Delikanlı, Torkan Shafighfard, Faramarz Bagherzadeh

“…This study highlights the potential of integrating optimization techniques with machine learning for reliable and interpretable structural predictions.…”
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A landslide susceptibility assessment method using SBAS-InSAR to optimize Bayesian network by Xinyu Gao, Bo Wang, Wen Dai, Yuanmin Liu, Yuanmin Liu

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Interpretability-guided RNA N6-methyladenosine modification site prediction with invertible neural networks by Guodong Li, Xiaorui Su, Yue Yang, Dongxu Li, Ziwen Cui, Xun Deng, Pengwei Hu, Lun Hu

“…Here, we introduce an interpretability-guided invertible neural network (m6A-IIN), a deep learning model to accurately identify m6A RNA modification sites by integrating both primary and secondary structure information under an invertible coupling framework. …”
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Innovative Framework for Historical Architectural Recognition in China: Integrating Swin Transformer and Global Channel–Spatial Attention Mechanism by Jiade Wu, Yang Ying, Yigao Tan, Zhuliang Liu

“…To gain deeper insights into the model’s decision-making process, we employed comprehensive interpretability methods including t-SNE (t-distributed Stochastic Neighbor Embedding), Grad-CAM (gradient-weighted class activation mapping), and multi-layer feature map analysis, revealing the model’s systematic feature extraction process from structural elements to material textures. …”
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SymbolNet: Bridging Latent Neural Representations and Symbolic Reasoning via Intermediate Feature Interpretation by Sungheon Jeong, Hyungjoon Kim, Yoojeong Song

“…This symbolic decoding bridges the model’s internal computations with human cognitive understanding, enabling structured and meaningful interpretation of AI behavior. …”
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Interpretable deep learning to uncover the molecular binding patterns determining TCR–epitope interaction predictions by Ceder Dens, Wout Bittremieux, Fabio Affaticati, Kris Laukens, Pieter Meysman

“…We demonstrate how interpretable AI techniques can be linked to the three-dimensional structure of molecules to offer novel insights into the factors that determine TCR affinity on a molecular level. …”
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Full cycle rice growth monitoring with dual-pol SAR data and interpretable deep learning by Ji Ge, Hong Zhang, Lu Xu, Wenjiang Huang, Jingling Jiang, Mingyang Song, Zihuan Guo, Chao Wang

“…Subsequently, a CGI is designed, combining fractional vegetation cover and leaf chlorophyll content to accurately depict rice growth status, capturing both the structural and physiological activity of rice. Finally, an interpretable DL architecture incorporating a feature selection explainer and a feature-aware enhanced segmentation model is introduced. …”
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Interpretable Clinical Decision-Making Application for Etiological Diagnosis of Ventricular Tachycardia Based on Machine Learning by Min Wang, Zhao Hu, Ziyang Wang, Haoran Chen, Xiaowei Xu, Si Zheng, Yan Yao, Jiao Li

“…<b>Objectives:</b> The aim is to propose a machine learning model for the etiological diagnosis of VT. Interpretable results based on models are compared with expert knowledge, and interpretable evaluation protocols for clinical decision-making applications are developed. …”
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An interpretable hybrid graph pooling scheme for system-scale adaptive small-signal stability assessment by Jiyu Huang, Waisheng Zheng, Yiping Chen, Yongzhe Li, Liukai Chen

“…Existing machine learning-based SSA methods fail under small system-scale changes and encounter efficiency loss when applied to large-scale systems. The model inference lacks enough interpretability for preventive controls. …”
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Fault damage zone and its unmanned aerial vehicle identification technology by CHEN Zebang, YUN Long, WANG Ju, TIAN Xiao

“…However, challenges remain in both the front-end (structural information acquisition) and back-end (structural information interpretation) processes, leaving ample room for future applications and advancements.…”
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Treegraph: tree architecture from terrestrial laser scanning point clouds by Wanxin Yang, Phil Wilkes, Matheus B. Vicari, Kate Hand, Kim Calders, Mathias Disney

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Effect of cytosolic pH on inward currents reveals structural characteristics of the proton transport cycle in the influenza A protein M2 in cell-free membrane patches of Xenopus oo... by Mattia L DiFrancesco, Ulf-Peter Hansen, Gerhard Thiel, Anna Moroni, Indra Schroeder

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Relational information framework, causality, unification of quantum interpretations and return to realism through non-ergodicity by Oded Shor, Felix Benninger, Andrei Khrennikov

“…Subsequently, we propose a statistical-dynamical model on this Minkowski-like parameter space, unifying Bohmian and Many Worlds interpretations of quantum theory in the framework of relational information. …”
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MEASUREMENT AND INTERPRETATION OF THE IMPACT OF SOME MACROECECONOMIC VARIABLES ON THE PERFORMANCE OF AGRICULTURAL SECTOR IN SELECTED DEVELOPING COUNTRIES by Alaa M. Abdullah

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Social Policy as a Key-Productive-Factor of an advanced sustainable development by Marco Ricceri

“…The first issue concerns the structural character of the current changes. The second issue concerns the peculiarity of globalisation’s process. …”
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Intractable prefrontal and limbic white matter network disruption in adolescents with drug-naïve nonsuicidal self-injury by Yuwei Chen, Xiongxiong Yang, Kaike Liao, Rui Yu, Xinyue Chen, Wenjing Zhang, Nian Liu

“…Network-based statistic (NBS) correction methods were used to assess structural connectivity within this network, and a generalized linear model was used to compare network metrics between NSSI and HCs, whereas paired t-tests were used to compare the same patients pre- and post-treatment. …”
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Factor structure and trends in SF-12v2 health-related quality of life scores among pre-and post-pandemic samples in Thailand: confirmatory factor analysis and Rasch analysis by Krittaphas Kangwanrattanakul

“…This study examined differences in mean scores, item interpretations due to differential item functioning (DIF), and factor structure invariance using confirmatory factor analysis (CFA) between the pre- and post-pandemic periods. …”
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A Next-generation Exoplanet Atmospheric Retrieval Framework for Transmission Spectroscopy (NEXOTRANS): Comparative Characterization for WASP-39 b Using JWST NIRISS, NIRSpec PRISM,... by Tonmoy Deka, Tasneem Basra Khan, Swastik Dewan, Priyankush Ghosh, Debayan Das, Liton Majumdar

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