ERBB3-related gene PBX1 is associated with prognosis in patients with HER2-positive breast cancer by Shufen Mo, Haiming Zhong, Weiping Dai, Yuanyuan Li, Bin Qi, Taidong Li, Yongguang Cai

“…Utilizing three distinct machine learning algorithms, we identified three signature genes-PBX1, IGHM, and CXCL13-that exhibited significant diagnostic value within the diagnostic model. …”
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Plant photosynthesis in basil (C3) and maize (C4) under different light conditions as basis of an AI-based model for PAM fluorescence/gas-exchange correlation by Isabell Pappert, Stefan Klir, Luca Jokic, Celine Ühlein, Khanh Tran Quoc, Ralf Kaldenhoff

“…To improve prediction accuracy, we applied a machine learning model. XGBoost, a gradient-boosted decision tree algorithm, efficiently captures nonlinear interactions between physiological and environmental parameters. …”
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Robust development of data-driven models for methane and hydrogen mixture solubility in brine by Kashif Saleem, Abhinav Kumar, K. D. V. Prasad, Ahmad Alkhayyat, T. Ramachandran, Protyay Dey, Navdeep Kaur, R. Sivaranjani, I. B. Sapaev, Mehrdad Mottaghi

“…In this paper, we aim to form robust data-driven intelligent algorithms founded on various machine learning methods of Support Vector Machine, Random Forest, AdaBoost, Decision Tree, K-nearest Neighbors, Multilayer Perceptron Artificial Neural Network and Convolutional Neural Network to model solubility of hydrogen/methane blend in brine under realistic conditions of underground hydrogen storage projects by utilizing an experimental dataset collected from the existing body of published research. …”
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Distinct brain atrophy progression subtypes underlie phenoconversion in isolated REM sleep behaviour disorderResearch in context by Stephen Joza, Aline Delva, Christina Tremblay, Andrew Vo, Marie Filiatrault, Max Tweedale, Jean-François Gagnon, Ronald B. Postuma, Alain Dagher, Johannes Klein, Michele Hu, Petr Dusek, Stanislav Marecek, Zsoka Varga, John-Paul Taylor, John T. O'Brien, Michael Firbank, Alan Thomas, Paul C. Donaghy, Stéphane Lehéricy, Marie Vidailhet, Jean-Christophe Corvol, ICEBERG Study Group, Richard Camicioli, Howard Chertkow, Simon Lewis, Elie Matar, Kaylena A. Ehgoetz Martens, Lachlan Churchill, Michael Sommerauer, Sinah Röttgen, Per Borghammer, Karoline Knudsen, Allan K. Hansen, Dario Arnaldi, Beatrice Orso, Pietro Mattioli, Luca Roccatagliata, Oury Monchi, Shady Rahayel, Isabelle Arnulf, Samir Bekadar, Eve Benchetrit, Alexis Brice, Vanessa Brochard, Alizé Chalançon, Benoit Colsch, Florence Cormier-Dequaire, Jean-Christophe Corvol, Virginie Czernecki, Bertrand Degos, Cécile Delorme, Pauline Dodet, Carole Dongmo-Kenfack, Marie-Odile Habert, Farid Ichou, Jonas Ihle, Cécile Galléa, Rahul Gaurav, Marie-Alexandrine Glachant, Manon Gomes, David Grabli, Elodie Hainque, Laetitia Jeancolas, Christelle Laganot, Stéphane Lehéricy, Suzanne Lesage, Smaranda Leu-Semenescu, Richard Levy, Valentine Maheo, Graziella Mangone, Louise Laure Mariani, Aurelie Méneret, Poornima Menon, Fanny Mochel, Vincent Perlbarg, Dijana Petrovska, Fanny Pineau, Nadya Pyatigorskaya, Sophie Rivaud-Pechoux, Emmanuel Roze, Sara Sambin, Julie Socha, Arthur Tenenhaus, Romain Valabregue, Marie Vidailhet, Lydia Yahia-Cherif

“…Brain atrophy was quantified using vertex-based cortical surface reconstruction and volumetric segmentation. The unsupervised machine learning algorithm, Subtype and Stage Inference (SuStaIn), was used to reconstruct spatiotemporal patterns of brain atrophy progression. …”
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Medical Device Failure Predictions Through AI-Driven Analysis of Multimodal Maintenance Records by Noorul Husna Abd Rahman, Khairunnisa Hasikin, Nasrul Anuar Abd Razak, Ayman Khallel Al-Ani, D. Jerline Sheebha Anni, Prabu Mohandas

“…In addition, sensitivity analysis is performed to select only the most significant parameters affecting the failure performance of the medical device. Then, four machine learning algorithms and three deep learning networks are evaluated to determine the best predictive model. …”
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Conv1D-LSTM: Autonomous Breast Cancer Detection Using a One-Dimensional Convolutional Neural Network With Long Short-Term Memory by Mitanshi Rastogi, Meenu Vijarania, Neha Goel, Akshat Agrawal, Cresantus N. Biamba, Celestine Iwendi

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Prospective Applications of Artificial Intelligence In Fetal Medicine: A Scoping Review of Recent Updates by Miskeen E, Alfaifi J, Alhuian DM, Alghamdi M, Alharthi MH, Alshahrani NA, Alosaimi G, Alshomrani RA, Hajlaa AM, Khair NM, Almuawi AM, Al-Jaber KH, Elrasheed FE, Elhassan K, Abbas M

“…This optimistic outlook underscores the need for further research and interdisciplinary partnerships to fully leverage AI’s potential in driving forward the practice of fetal medicine.Keywords: artificial intelligence, fetal medicine, prenatal care, machine learning, fetal monitoring, Bisha, Saudi Arabia…”
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Detection and prevalence of avian influenza epidemic in the southwest of Poyang Lake and analysis of the influence of meteorological factors by Kang Fang, Xiansheng Ni, Xi Wang, Wentao Song, Zhiqiang Deng, Zeyu Zhao, Wei Hua, Zhizhong Zeng, Wei Wang, Qianqian Si, Jiang Wu, Bo Zhang, Ping Zhang, Hui Li, Tianmu Chen

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Innovations in animal health: artificial intelligence-enhanced hematocrit analysis for rapid anemia detection in small ruminants by Aftab Siddique, Sudhanshu S. Panda, Sophia Khan, Seymone T. Dargan, Savana Lewis, India Carter, Jan A. Van Wyk, Ajit K. Mahapatra, Eric R. Morgan, Thomas H. Terrill

“…Using artificial intelligence-powered machine learning algorithms, an advanced, easy-to-use sensor was developed for rapidly alerting farmers as to low red blood cell count of their animals in this way to enable timely medical intervention. …”
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Integrating Gut Microbiome and Metabolomics with Magnetic Resonance Enterography to Advance Bowel Damage Prediction in Crohn’s Disease by Huang L, Meng J, Lin S, Peng Z, Zhang R, Shen X, Zheng W, Zheng Q, Wu L, Wang X, Wang Y, Mao R, Sun C, Li X, Feng ST

“…The relationships between microbial/metabolic factors and MRE features were explored using correlation and mediation analyses. Seven machine learning algorithms, each paired with seven distinct combinations of multi-omics features, were evaluated using nested 5-fold cross-validation to construct an optimal prediction model. …”
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Comorbidities associated with fetal alcohol spectrum disorders in the United States by Brandon K. Attell, Angela B. Snyder, Claire Coles, Julie Kable

“…Employing a novel unsupervised machine learning algorithm applied to a nationally representative hospital discharge database, we found 57 distinct comorbidities that frequently occurred among FASD cases, in addition to a set of 144 complex overlapping comorbidity patterns. …”
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EnSCAN: ENsemble Scoring for prioritizing CAusative variaNts across multiplatform GWASs for late-onset alzheimer’s disease by Onur Erdogan, Cem Iyigun, Yeşim Aydın Son

“…The Genome-Wide Association Studies (GWAS) enable the exploration of individual variants' statistical interactions at candidate loci, but univariate analysis overlooks interactions between variants. Machine learning (ML) algorithms can capture hidden, novel, and significant patterns while considering nonlinear interactions between variants to understand the genetic predisposition for complex genetic disorders. …”
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Accurately assessing congenital heart disease using artificial intelligence by Khalil Khan, Farhan Ullah, Ikram Syed, Hashim Ali

“…These ML-based models can help healthcare professionals identify high-risk infants and ensure timely and appropriate care. In addition, ML algorithms excel at detecting and analyzing complex patterns that can be overlooked by human clinicians, thereby enhancing diagnostic accuracy. …”
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Theoretical approaches to detecting anomalies in meter readings in scientific literature by D.V. Furikhata, T.A. Vakalyuk

“…Particular attention is paid to analysing the effectiveness of various machine learning algorithms for anomaly detection. …”
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AI generations: from AI 1.0 to AI 4.0 by Jiahao Wu, Hengxu You, Jing Du

“…Each AI generation is driven by shifting priorities among algorithms, computing power, and data. AI 1.0 accompanied breakthroughs in pattern recognition and information processing, fueling advances in computer vision, natural language processing, and recommendation systems. …”
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Integrative multi-omics analysis reveals the role of toll-like receptor signaling in pancreatic cancer by Jie Peng, Jiaao Sun, Youfeng Yu, Qihang Yuan, Yong Zhang

“…Finally, we combined a series of machine learning algorithms to build a pancreatic cancer prognosis model that includes four genes (NT5E, TGFBI, ANLN, and FAM83A). …”
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Illuminating the Hierarchical Segmentation of Faults Through an Unsupervised Learning Approach Applied to Clouds of Earthquake Hypocenters by E. Piegari, G. Camanni, M. Mercurio, W. Marzocchi

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Recent Advances in Resistive Gas Sensors: Fundamentals, Material and Device Design, and Intelligent Applications by Peiqingfeng Wang, Shusheng Xu, Xuerong Shi, Jiaqing Zhu, Haichao Xiong, Huimin Wen

“…Machine learning (ML) algorithms have enabled intelligent design of novel sensing materials, optimized multi-gas identification, and enhanced data reliability in complex environments. …”
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Rapid detection of antibiotic resistance in Burkholderia pseudomallei using MALDI-TOF mass spectrometry by Nut Nithimongkolchai, Yothin Hinwan, Kanwara Trisakul, Lumyai Wonglakorn, Ploenchan Chetchotisakd, Auttawit Sirichoat, Arnone Nithichanon, Sorujsiri Chareonsudjai, Pisit Chareonsudjai, Jody Phelan, Taane G. Clark, Kiatichai Faksri

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A novel multi-step methodology for stochastic simulation of streamflow time series using PcStream clustering by Shalini Balaram, Roshan Srivastav, K Srinivasan

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