Gut microbiome-derived bacterial extracellular vesicles in patients with solid tumours by Surbhi Mishra, Mysore Vishakantegowda Tejesvi, Jenni Hekkala, Jenni Turunen, Niyati Kandikanti, Anna Kaisanlahti, Marko Suokas, Sirpa Leppä, Pia Vihinen, Hanne Kuitunen, Kaisa Sunela, Jussi Koivunen, Arja Jukkola, Ilja Kalashnikov, Päivi Auvinen, Okko-Sakari Kääriäinen, T. Peñate Medina, O. Peñate Medina, Juha Saarnio, Sanna Meriläinen, Tero Rautio, Raila Aro, Reetta Häivälä, Juho Suojanen, Mikael Laine, Pande Putu Erawijattari, Leo Lahti, Peeter Karihtala, Terhi S. Ruuska, Justus Reunanen

“…We also investigated the gut microbiomes of feces from patients and controls using 16S sequencing and used machine learning to classify the samples into patients and controls based on their bEVs and fecal microbiomes. …”
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A Multi-Kernel Mode Using a Local Binary Pattern and Random Patch Convolution for Hyperspectral Image Classification by Wei Huang, Yao Huang, Zebin Wu, Junru Yin, Qiqiang Chen

“…In order to improve classification performance while reducing costs, this article proposes a multikernel method based on a local binary pattern and random patches (LBPRP-MK), which integrates a local binary pattern (LBP) and deep learning into a multiple-kernel framework. …”
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Molecular mechanisms explaining sex-specific functional connectivity changes in chronic insomnia disorder by Liyong Yu, Zhifu Shen, Wei Wei, Zeyang Dou, Yucai Luo, Daijie Hu, Wenting Lin, Guangli Zhao, Xiaojuan Hong, Siyi Yu

“…Additionally, we simulated the impact of sex differences in rsFC with different sex compositions in our dataset and employed machine learning classifiers to distinguish CID from healthy controls based on sex-specific rsFC data. …”
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Extraction of built-up areas using Sentinel-1 and Sentinel-2 data with automated training data sampling and label noise robust cross-fusion neural networks by Yu Li, Patrick Matgen, Marco Chini

“…In recent years, there has been a growing interest in employing supervised machine learning and deep learning methods to map built-up areas using satellite SAR and optical data. …”
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Issues of Designing Effective Educational Programs in the Field of “Applied Informatics” in the Conditions of Innovative Development by Yu. F. Telnov, M. S. Gasparian, M. A. Filyuk

“…The solution to this problem is seen in the development of methods and tools for generating educational-methodical and organizational-administrative content based on the digital repository of the electronic learning system through knowledge management technologies that ensure the adaptability of the educational process using the ontological approach. …”
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Reaction Pathway Differentiation Enabled Fingerprinting Signal for Single Nucleotide Variant Detection by Huixiao Yang, Linghao Zhang, Xinmiao Kang, Yunpei Si, Ping Song, Xin Su

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A hybrid fuzzy logic–Random Forest model to predict psychiatric treatment order outcomes: an interpretable tool for legal decision support by Alexandre Hudon, Alexandre Hudon, Alexandre Hudon, Alexandre Hudon

“…This study aims to develop and evaluate a hybrid fuzzy logic–machine learning model to predict such outcomes and identify important influencing factors.MethodsA retrospective dataset of 176 Superior Court judgments rendered in Quebec in 2024 was curated from SOQUIJ, encompassing demographic, clinical, and legal variables. …”
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Assessing CNN and Semantic Segmentation Models for Coarse Resolution Satellite Image Classification in Subcontinental Scale Land Cover Mapping by Tesfaye Adugna, Wenbo Xu, Jinlong Fan, Haitao Jia, Xin Luo

“…Based on studies using high-medium resolution images, convolutional neural networks (CNNs) and semantic segmentation have shown superiority over classical machine learning (ML), particularly in small-scale mapping. …”
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An interpretable stacking ensemble model for high-entropy alloy mechanical property prediction by Songpeng Zhao, Zeyuan Li, Changshuai Yin, Zhaofu Zhang, Teng Long, Jingjing Yang, Ruyue Cao, Yuzheng Guo

“…In this study, we propose a stacking learning-based machine learning framework to improve the accuracy and robustness of HEA mechanical property predictions. …”
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Development of data driven models to accurately estimate density of fatty acid ethyl esters by Walid Abdelfattah, Munthar Kadhim Abosaoda, Hardik Doshi, H. S. Shreenidhi, Manoranjan Parhi, Devendra Singh, Prabhjot Singh, Abdolali Yarahmadi Kandahari

“…The objective of this study is to construct advanced predictive algorithms using various machine learning methods, including AdaBoost, Decision Trees, KNN, Random Forests, Ensemble Learning, CNN, and SVR. …”
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Optimizing blood-brain barrier permeability in KRAS inhibitors: A structure-constrained molecular generation approach by Xia Sheng, Yike Gui, Jie Yu, Yitian Wang, Zhenghao Li, Xiaoya Zhang, Yuxin Xing, Yuqing Wang, Zhaojun Li, Mingyue Zheng, Liquan Yang, Xutong Li

“…To support this, we incorporate a specialized KRAS BBB predictor based on active learning and an affinity predictor employing comparative learning models. …”
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TopoRSNet: A Unique Approach to Maintaining Topological Features for Digital Rock Image Rescaling With Minimal Quality Degradation by Kunning Tang, Yufu Niu, Ying DaWang, Vanessa Robins, Peyman Mostaghimi, Mark Lindsay, Ryan T. Armstrong

“…The efficacy of TopoRSNet is validated on two common geological rocks, and the results are compared to other rescaling methods. This method enables scalable analysis of multiscale rock features, allowing for broader integration of 3D imaging in geoscientific modeling, simulation, and machine learning workflows.…”
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Correction of Batch Effect in Gut Microbiota Profiling of ASD Cohorts from Different Geographical Origins by Matteo Scanu, Federica Del Chierico, Riccardo Marsiglia, Francesca Toto, Silvia Guerrera, Giovanni Valeri, Stefano Vicari, Lorenza Putignani

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AI-supported versus manual microscopy of Kato-Katz smears for diagnosis of soil-transmitted helminth infections in a primary healthcare setting by Joar von Bahr, Antti Suutala, Hakan Kucukel, Harrison Kaingu, Felix Kinyua, Martin Muinde, Kevan Osundwa, Wigina Ronald, Jackson Muinde, Billy Ngasala, Mikael Lundin, Andreas Mårtensson, Nina Linder, Johan Lundin

“…In this study, portable whole-slide scanners and deep learning-based artificial intelligence (AI) were deployed in a primary healthcare setting in Kenya. …”
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Five-Year Evaluation of Sentinel-2 Cloud-Free Mosaic Generation Under Varied Cloud Cover Conditions in Hawai’i by Francisco Rodríguez-Puerta, Ryan L. Perroy, Carlos Barrera, Jonathan P. Price, Borja García-Pascual

“…We conducted a comparative analysis of three cloud-masking methods: two Google Earth Engine algorithms (CloudScore+ and s2cloudless) and a new proprietary deep learning-based algorithm (L3) applied to Sentinel-2 imagery. …”
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GNNs and ensemble models enhance the prediction of new sRNA-mRNA interactions in unseen conditions by Shani Cohen, Lior Rokach, Isana Veksler-Lublinsky

“…Our comprehensive feature importance analysis revealed the complexity of sRNA-mRNA interactions across environmental conditions, underscoring the significance of RNA sequence composition and duplex structure characteristics, like base pairing and energy factors; findings that align with biological evidence from previous studies. …”
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Metabolome Profiling and Predictive Modeling of Dark Green Leaf Trait in Bunching Onion Varieties by Tetsuya Nakajima, Mari Kobayashi, Masato Fuji, Kouei Fujii, Mostafa Abdelrahman, Yasumasa Matsuoka, Jun’ichi Mano, Muneo Sato, Masami Yokota Hirai, Naoki Yamauchi, Masayoshi Shigyo

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Teaching written foreign language communication in a nonlinguistic university by Zara Mukhamcherievna Shadzhe

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Lavender hydrosol analysis using UV spectroscopy data and partial least squares regression by Sára Preiner, Bálint Levente Tarcsay, Dóra Pethő, Norbert Miskolczi

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Dual‐fluorescence imaging and automated trophallaxis detection for studying multi‐nutrient regulation in superorganisms by Lior Baltiansky, Einav Sarafian‐Tamam, Efrat Greenwald, Ofer Feinerman

“…Additionally, our image‐based deep learning algorithm for automatic detection of ant trophallaxis events efficiently yields a detailed record of all food‐transfer interactions. …”
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