Characteristics and Cluster Analysis of 18,030 Sepsis Patients Who Were Admitted to Thailand’s Largest National Tertiary Referral Center during 2014–2020 to Identify Distinct Subty... by Phuwanat Sakornsakolpat, Surat Tongyoo, Chairat Permpikul

“…This study aimed to investigate the demographic, clinical, and laboratory characteristics of sepsis patients who were admitted to our center during 2014–2020 and to employ cluster analysis, which is a type of machine learning, to identify distinct types of sepsis in Thai population. …”
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Pilot study: Initial investigation suggests differences in EMT-associated gene expression in breast tumor regions by Kylie L. King, Hamed Abdollahi, Zoe Dinkel, Alannah Akins, Homayoun Valafar, Heather Dunn

“…This pilot study aimed to evaluate gene expression in TNBC samples from patients who identified as African American and Caucasian using traditional statistical methods and emerging Machine Learning (ML) approaches. ML enables the analysis of complex datasets and the extraction of useful information from small datasets. …”
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Real-world clinical multi-omics analyses reveal bifurcation of ER-independent and ER-dependent drug resistance to CDK4/6 inhibitors by Zhengyan Kan, Ji Wen, Vinicius Bonato, Jennifer Webster, Wenjing Yang, Vladimir Ivanov, Kimberly Hyunjung Kim, Whijae Roh, Chaoting Liu, Xinmeng Jasmine Mu, Jennifer Lapira-Miller, Jon Oyer, Todd VanArsdale, Paul A. Rejto, Jadwiga Bienkowska

“…ER-dependent drug resistance mechanisms. Machine learning models predict therapeutic dependency on ESR1 and CDK4 among ER-dependent tumors and CDK2 dependency among ER-independent tumors, confirmed by experimental validation.…”
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Identifying Incident Causal Factors to Improve Aviation Transportation Safety: Proposing a Deep Learning Approach by Tianxi Dong, Qiwei Yang, Nima Ebadi, Xin Robert Luo, Paul Rad

“…The solution we propose has multilabel capability and is automated and customizable, and it is more accurate and adaptable than traditional machine learning methods in extant research. This novel application of deep learning algorithms to the incident reporting system can efficiently improve aviation safety.…”
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Enhanced Learning Behaviors and Ability Knowledge Tracing by Fanglan Ma, Changsheng Zhu, Peng Lei, Peiwen Yuan

“…Knowledge tracing (KT) aims to understand the evolution of students’ knowledge states during learning using machine learning techniques. While KT has made significant strides with deep learning techniques, a gap remains reflecting students’ actual knowledge level—the significant effects of students’ learning behaviors and abilities are omitted, which can reflect their knowledge acquisition more deeply and ensure the reliability of the response. …”
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IAR 2.0: An Algorithm for Refining Inconsistent Annotations for Time-Series Data Using Discriminative Classifiers by Einari Vaaras, Manu Airaksinen, Okko Rasanen

“…The performance of discriminative machine-learning classifiers, such as neural networks, is limited by training label inconsistencies. …”
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Toll-like receptors in atopic dermatitis: pathogenesis and therapeutic implications by Ahmad Vafaeian, Fateme Rajabi, Nima Rezaei

“…Future directions include CRISPR-based gene editing to understand TLR functions, development of specific TLR modulators for targeted therapy, and machine learning applications to predict drug responses and identify novel ligands. …”
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Short‐Term Memory and Regional Climate Drive City‐Scale Water Demand in the Contiguous US by Wenjin Hao, Andrea Cominola, Andrea Castelletti

“…We develop a causal discovery framework based on a Neural Granger Causal (NGC) model, a machine learning approach that identifies nonlinear causal relationships between determinants and water demand, enabling comprehensive water demand determinants discovery and water demand forecasting across the CONUS. …”
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Determination of the area index of lettuce leaves with a monocular camera by Laimonas Kairiūkštis, Başak Yalçıner, Emre Özkul

“…This study showcases the potential of computer vision and machine learning algorithms in transforming traditional agricultural practices into more efficient and data-driven processes. …”
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System-Level Digital Twin Modeling for Underwater Wireless IoT Networks by Lei Wang, Lei Yan, Xinbin Li, Song Han

“…Federated Learning (FL) presents a distributed machine learning paradigm that enables devices within underwater wireless IoT networks to collaboratively refine a DT model. …”
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A systematic review of passive data for remote monitoring in psychosis and schizophrenia by Siân Bladon, Emily Eisner, Sandra Bucci, Anuoluwapo Oluwatayo, Glen P. Martin, Matthew Sperrin, John Ainsworth, Sophie Faulkner

“…Mixed effects models were used in 21 studies and time-series and machine-learning methods were used in 18 studies. Reporting of methods to process and analyse data was inconsistent, highlighting a need to improve the standardisation of methods and reporting in this area of research.…”
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Fake News Detection and Classification: A Comparative Study of Convolutional Neural Networks, Large Language Models, and Natural Language Processing Models by Konstantinos I. Roumeliotis, Nikolaos D. Tselikas, Dimitrios K. Nasiopoulos

“…In this study, the effectiveness is investigated of advanced machine learning models—convolutional neural networks (CNNs), bidirectional encoder representations from transformers (BERT), and generative pre-trained transformers (GPTs)—for robust fake news classification. …”
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Visual Gradation of Biological Soil Crust Development: A Simple and Effective Recording Method by Xinyu Zhang, Ping He, Jie Xu

“…Future research should focus on expanding the grading method to include lichen crust, refining it across diverse ecosystems, and exploring the integration of advanced technologies such as hyperspectral imaging and machine learning to automate and improve the classification process. …”
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Advances in Biomedical Missing Data Imputation: A Survey by Miriam Barrabes, Maria Perera, Victor Novelle Moriano, Xavier Giro-I-Nieto, Daniel Mas Montserrat, Alexander G. Ioannidis

“…These methods range from traditional statistical analysis to more modern approaches such as discriminative machine learning models and deep generative networks. This survey paper provides a comprehensive overview of the extensive literature on missing data imputation techniques, with a specific focus on applications in genomics, single-cell RNA sequencing, health records, and medical imaging. …”
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SViG: A Similarity-Thresholded Approach for Vision Graph Neural Networks by Ismael Elsharkawi, Hossam Sharara, Ahmed Rafea

“…Image representation in computer vision is a long-standing problem that has a significant impact on any machine learning model performance. There have been multiple attempts to tackle this problem that were introduced in the literature, starting from traditional Convolutional Neural Networks (CNNs) to Vision Transformers and MLP-Mixers that were more recently introduced to represent images as sequences. …”
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TDP-43 as a potential retinal biomarker for neurodegenerative diseases by Margit Glashutter, Margit Glashutter, Printha Wijesinghe, Joanne A. Matsubara, Joanne A. Matsubara

“…Future directions for the TDP-43 as a retinal biomarker for NDDs include retinal tracers, hyperspectral imaging, oculomics, and machine learning development.…”
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A generative deep neural network for pan-digestive tract cancer survival analysis by Lekai Xu, Tianjun Lan, Yiqian Huang, Liansheng Wang, Junqi Lin, Xinpeng Song, Hui Tang, Haotian Cao, Hua Chai

“…With the rapid advancement of artificial intelligence, various machine learning algorithms have been successfully applied in this field. …”
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Serum and Cerebrospinal Fluid Cytokine Biomarkers for Diagnosis of Multiple Sclerosis by Ekaterina Martynova, Mehendi Goyal, Shikhar Johri, Vinay Kumar, Timur Khaibullin, Albert A. Rizvanov, Subhash Verma, Svetlana F. Khaiboullina, Manoj Baranwal

“…ANOVA-based feature and Pearson correlation coefficient scores were calculated to select the features which were used as input by machine learning models, to predict and classify MS. Results. …”
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Deciphering sepsis: transforming diagnosis and treatment through systems immunology by Robert E. W. Hancock, Andy An, Claudia C. dos Santos, Amy H. Y. Lee

“…Systems immunology methods, including multiomics (notably RNA sequencing transcriptomics), machine learning, and network biology analysis, have the potential to transform the management paradigm toward precision approaches. …”
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Classifying IoT Botnet Attacks With Kolmogorov-Arnold Networks: A Comparative Analysis of Architectural Variations by Phuc Hao do, Tran Duc Le, Truong Duy Dinh, van Dai Pham

“…This study aims to evaluate the effectiveness of Kolmogorov-Arnold Networks (KANs) and their architectural variations in classifying IoT botnet attacks, comparing their performance with traditional machine learning and deep learning models. We conducted a comparative analysis of five KAN architectures, including Original-KAN, Fast-KAN, Jacobi-KAN, Deep-KAN, and Chebyshev-KAN, against models like Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRU). …”
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