Metabolic profile changes in patients with rheumatoid arthritis detected using mass spectrometry by Xue Wu, Qi Yang, Shanshan Liu, Peng Yang, Zhengqi Liu, Zhitu Zhu

“…Conclusions The selected biomarkers have the potential to improve the detection of RA, and may offer insights into the intervention strategies to susceptible at-risk populations of developing RA.…”
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High-content screening (HCS) workflows for FAIR image data management with OMERO by Riccardo Massei, Wibke Busch, Beatriz Serrano-Solano, Matthias Bernt, Stefan Scholz, Elena K. Nicolay, Hannes Bohring, Jan Bumberger

“…These workflows facilitate the management of large amounts of data, reduce the risk of human error, and improve the efficiency and effectiveness of image data management. …”
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Predicting depression in healthy young adults: A machine learning approach using longitudinal neuroimaging data by Ailing Zhang, Haobo Zhang

“…Support vector machine and random forest algorithms were then used to construct prediction models. …”
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A Novel Immune-Related Three-Gene Signature and Immune Infiltration Insights in Psoriasis and Chronic Kidney Disease by Yin T, Zhang T, Ma L

“…Subsequently, six hub genes were determined using five topological algorithms. The responses of these two diseases exhibited similar changes in immune reactions. …”
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Development of Electronic Nose as a Complementary Screening Tool for Breath Testing in Colorectal Cancer by Chih-Dao Chen, Yong-Xiang Zheng, Heng-Fu Lin, Hsiao-Yu Yang

“…The objective of this study was to provide an assessment of the diagnostic performance of an electronic nose serving as a complementary screening tool to improve current screening programs in clinical settings. (2) Methods: We conducted a case–control study that included patients from a medical center with colorectal cancer and non-colorectal cancer controls. …”
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Integrating equity, diversity, and inclusion throughout the lifecycle of artificial intelligence for healthcare: a scoping review. by Ting Wang, Elham Emami, Dana Jafarpour, Raymond Tolentino, Genevieve Gore, Samira Abbasgholizadeh Rahimi

“…Previous research has shown that AI models improve when socio-demographic factors such as gender and race are considered. …”
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Development of an optimized deep learning model for predicting slope stability in nano silica stabilized soils by Ishwor Thapa, Sufyan Ghani, Prabhu Paramasivam, Mitiku Adare Tufa

“…The results show that RNN-CNN-LSTM, optimized through OPTUNA algorithms, overcomes conventional machine learning models and achieves an accuracy of 99.4% on unseen test data, supported by stable validation trends and robust predictive performance. …”
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Assessing change point detection methods to enable robust detection of early stage Artisanal and Small-Scale mining (ASM) in the tropics using Sentinel-1 time series data by Mensah Isaac Obour, Barrett Brian, Cahalane Conor

“…This study utilised a time series smoothing technique to improve Sentinel-1 (S-1) SAR time series data, reducing SAR noise and atmospheric effects from heavy rainfall for early ASM activity detection. …”
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Machine learning based method for forecasting short-term passenger flow in urban rail stations by HU Mingwei, SHI Xiaolong, WU Wenlin, HE Guoqing

“…It also plays a prominent part in solving urban traffic problems. In order to improve the operation efficiency of the urban rail transit system and achieve the goal of smart operations, this paper applies machine learning algorithms and completes the feature engineering of urban rail transit passenger flow data in terms of time, space and external factors. …”
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MODELING THE BEHAVIOR OF ENTERTAINMENT CONTENT CONSUMERS TAKING INTO ACCOUNT DIGITAL TECHNOLOGIES by Olga Zyma

“…The research aims to model the behavior of young people as consumers of entertainment content, taking into account digital technologies to improve the psychological well-being of youth and enhance the quality of media products. …”
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Range Space and Similarity Transformation Using Symbolic Mathematics in Python by Chainarong Wisassakwichai, Prasert Phaochoo

“…The effectiveness of these classes has been validated through numerical results, which, when compared to MATLAB functions (null(), pinv(), sym(), and jordan()), demonstrate that the exact symbolic solutions from both classes significantly improve numerical precision for selected application examples from numerical algorithms, power systems and linear algebra. …”
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Estimating canopy height in tropical forests: Integrating airborne LiDAR and multi-spectral optical data with machine learning by Brianna J. Pickstone, Hugh A. Graham, Andrew M. Cunliffe

“…This study aims to compare the performance of three machine learning algorithms (Multiple Linear Regression (MLR), Random Forest (RF), and Convolutional Neural Networks (CNN)) when using PlanetScope and Sentinel-2 imagery to improve the accuracy of height predictions. …”
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PLOD3 as a novel oncogene in prognostic and immune infiltration risk model based on multi-machine learning in cervical cancer by Lingling Qiu, Xiuchai Qiu, Xiaoyi Yang

“…Abstract Cervical carcinoma (CC) remains a significant global health issue despite advancements in screening and treatment. To improve prognostic accuracy and therapeutic strategies, we developed a multi-machine learning prognostic model based on metabolic-associated genes. …”
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A Machine Learning and Remote Sensing‐Based Model for Algae Pigment and Dissolved Oxygen Retrieval on a Small Inland Lake by Maxwell R. W. Beal, Mutlu Özdoğan, Paul J. Block

“…Machine learning methods are implemented with existing algorithms to model chlorophyll‐a, phycocyanin, and Pc:Chla. …”
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An Overview of Deep Learning Applications in Groundwater Level Modeling: Bridging the Gap between Academic Research and Industry Applications by Ahmed Shakir Ali Ali, Farhad Jazaei, Peyman Babakhani, Muhammad Masood Ashiq, Alireza Bakhshaee, Brian Waldron

“…Therefore, this article identifies shortcomings in recent literature on DL for GWL studies and suggests addressing these issues to improve practical application in real-world settings.…”
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Deep learning radiomics based on MRI for differentiating tongue cancer T - staging by Zhaoyi Lu, Bowen Zhu, Hang Ling, Xi Chen

“…Conclusion DLRresnet18 and DLRresnet50 models significantly improve T-stage prediction accuracy over traditional radiomics, reducing subjective interpretation errors and supporting personalized treatment planning. …”
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Polygenic risk score for acute rejection based on donor-recipient non-HLA genotype mismatch. by Rui Cao, David P Schladt, Casey Dorr, Arthur J Matas, William S Oetting, Pamala A Jacobson, Ajay Israni, Jinbo Chen, Weihua Guan

“…Additional work will be needed to understand the role of SNPs included in the PRS and to further improve donor-recipient genetic matching algorithms. …”
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Robust EEG Characteristics for Predicting Neurological Recovery from Coma After Cardiac Arrest by Meitong Zhu, Meng Xu, Meng Gao, Rui Yu, Guangyu Bin

“…Significance: Our research identifies key electroencephalographic (EEG) biomarkers, including low-frequency connectivity and burst suppression thresholds, to improve early and objective prognosis assessments. …”
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Neighbor Similarity Based Agglomerative Method for Community Detection in Networks by Jianjun Cheng, Xing Su, Haijuan Yang, Longjie Li, Jingming Zhang, Shiyan Zhao, Xiaoyun Chen

“…Therefore, we merge some of them in an efficient approach in the second phase to improve the quality of the resulting community structure. …”
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Integrating direct observation and environmental DNA data to enhance species distribution models in riverine environments by Luca Carraro

“…Here, I propose a framework to leverage both data types to improve estimates of a taxon’s spatial distribution. …”
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