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Predicting the effectiveness of chemotherapy treatment in lung cancer utilizing artificial intelligence-supported serum N-glycome analysis

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Computers in Biology and Medicine 2025年 186卷 109681-109681页

作者： Torok, Rebeka Meszaros, Brigitta Gombas, Veronika Vathy-Fogarassy, Agnes Szabo, Miklos Csanky, Eszter Jarvas, Gabor Guttman, Andras Research Institute of Biomolecular and Chemical Engineering University of Pannonia Veszprem Hungary Horváth Csaba Memorial Laboratory of Bioseparation Sciences Research Center for Molecular Medicine Doctoral School of Molecular Medicine Faculty of Medicine University of Debrecen Debrecen Hungary Department of Computer Science and Systems Technology University of Pannonia Veszprem Hungary Department of Pulmonology Borsod Academic County Hospital Miskolc Hungary

An efficient novel approach is introduced to predict the effectiveness of chemotherapy treatment in lung cancer by monitoring the serum N-glycome of patients combined with artificial intelligence-based data analysis. The study involved thirty-three lung cancer patients undergoing chemotherapy treatments. Serum samples were taken before and after the treatment. The N-linked oligosaccharides were enzymatically released, fluorophore-labeled, and analyzed by capillary electrophoresis with laser-induced fluorescence detection. The resulting electropherograms were thoroughly processed and evaluated by artificial intelligence-based classifiers, i.e., utilizing a machine learning algorithm to categorize the data into two (binary) classes. The classifier analysis method revealed a strong association between the structural changes in the N-glycans and the outcomes of the chemotherapy treatments (ROC >0.9). This novel combination of bioanalytical and AI methods provided a precise and rapid tool for predicting the effectiveness of chemotherapy. © 2025 The Authors

关键词： Lung cancer

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Explainable machine learning for predicting tensile properties of aluminum alloys in the laser powder bed fusion process

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Materials Science and Engineering: A 2025年 939卷

作者： Shah, Abdul Wahid Wang, Kang Siddique, Jabir Ali Hussain, Javid Li, Wenfang Materials Science and Engineering Dongguan University of Technology Dongguan523000 China Mechanical and Automotive Engineering South China University of Technology Guangzhou510640 China Industrial Technology University of Science and Technology Daejeon Korea Republic of Engineering and Built Environment Griffith University Gold Coast4215 Australia

The rapid solidification and unique thermal gradients inherent to the laser powder bed fusion of metals (PBF-LB/M) process limit the suitability of conventional aluminum (Al) alloys, necessitating the optimization of existing alloys or the development of new compositions to achieve the desired tensile properties while ensuring good processability. Experimental exploration of alloy compositions is labor-intensive, costly, and time-consuming. Machine learning (ML) offers a cost-effective, flexible approach to streamline alloy design and accelerate advancements in AM technologies. This study introduces a data-driven predictive framework for predicting tensile properties of Al alloys for PBF-LB/M. To address the limited data on LPBF of Al alloys and the restricted range of alloy systems investigated, data of conventional Al alloys (including cast and wrought alloys) and laser-directed energy deposition (DED-LB/M) built Al alloys were also included, alongside PBF-LB/M data. The dataset incorporates a comprehensive pool of features such as alloy composition, processing parameters, grain size, and elemental properties. The Pearson correlation coefficient (PCC) with feature importance-based feature selection was implemented to balance model complexity and accuracy via reducing the dimensionality and overfitting. The resulting ML framework demonstrates excellent predictive accuracy and generalizability, successfully extending its applicability to unseen alloy systems. This framework offers a reliable tool for optimizing Al alloy designs, significantly reducing reliance on costly experimental trials. The inclusion of Explainable AI provided detailed interpretability, elucidating the influence of individual features on model predictions, ensuring the predictions were scientifically grounded. © 2025 Elsevier B.V.

关键词： Aluminum alloys

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