Digital mapping of peat thickness and extent in Finland using remote sensing and machine learning by Jonne Pohjankukka, Timo A. Räsänen, Timo P. Pitkänen, Arttu Kivimäki, Ville Mäkinen, Tapio Väänänen, Jouni Lerssi, Aura Salmivaara, Maarit Middleton

“…We carefully split the reference data into training and test sets, allowing for independent and robust model validation. Feature selection included an initial screening for multicollinearity using correlation-based feature pruning, followed by final selection using a genetic algorithm. …”
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Postpartum depression in Northeastern China: a cross-sectional study 6 weeks after giving birth by XuDong Huang, LiFeng Zhang, ChenYang Zhang, Jing Li, ChenYang Li

“…Feature importance was ranked via a random forest model based on the change in ROC-AUC after predictor removal. …”
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Drug–target interaction prediction by integrating heterogeneous information with mutual attention network by Yuanyuan Zhang, Yingdong Wang, Chaoyong Wu, Lingmin Zhan, Aoyi Wang, Caiping Cheng, Jinzhong Zhao, Wuxia Zhang, Jianxin Chen, Peng Li

“…DrugMAN uses a graph attention network-based integration algorithm to learn network-specific low-dimensional features for drugs and target proteins by integrating four drug networks and seven gene/protein networks collected by a certain screening conditions, respectively. …”
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Machine Learning for Predicting Zearalenone Contamination Levels in Pet Food by Zhenlong Wang, Wei An, Jiaxue Wang, Hui Tao, Xiumin Wang, Bing Han, Jinquan Wang

“…Other algorithms showed moderate accuracy, ranging from 77.1% to 84.8%. …”
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Interpretable machine learning for depression recognition with spatiotemporal gait features among older adults: a cross-sectional study in Xiamen, China by Shaowu Lin, Sicheng Li, Ya Fang

“…The developed machine learning models with high predictive accuracy, suggest the potential of Kinect-based gait assessment as a real-time and cost-effective screening tool for older adults with depressive symptoms.…”
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Detection of Undiagnosed Liver Cirrhosis via Artificial Intelligence-Enabled Electrocardiogram (DULCE): Rationale and design of a pragmatic cluster randomized clinical trial by Amy Olofson, Ryan Lennon, Blake Kassmeyer, Kan Liu, Zacchi I. Attia, David Rushlow, Puru Rattan, Joseph C. Ahn, Paul A. Friedman, Alina Allen, Patrick S. Kamath, Vijay H. Shah, Peter A. Noseworthy, Douglas A. Simonetto

“…A novel electrocardiogram (ECG)-enabled deep learning model trained for detection of advanced chronic liver disease (CLD) has demonstrated promising results and it may be used for screening of advanced CLD in primary care. …”
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Identification of 17 novel epigenetic biomarkers associated with anxiety disorders using differential methylation analysis followed by machine learning-based validation by Yoonsung Kwon, Asta Blazyte, Yeonsu Jeon, Yeo Jin Kim, Kyungwhan An, Sungwon Jeon, Hyojung Ryu, Dong-Hyun Shin, Jihye Ahn, Hyojin Um, Younghui Kang, Hyebin Bak, Byoung-Chul Kim, Semin Lee, Hyung-Tae Jung, Eun-Seok Shin, Jong Bhak

“…Subsequent validation of identified biomarkers employed an artificial intelligence-based risk prediction models: a linear calculation-based methylation risk score model and two tree-based machine learning models: Random Forest and XGBoost. …”
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Ekyeyo Mobile Application by Nkuba, Blair, Kansiime, Daniel

“…The app incorporates advanced search algorithms, tailored job recommendations, and streamlined candidate screening to improve job-matching accuracy. …”
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Opportunities and Challenges of Cardiovascular Disease Risk Prediction for Primary Prevention Using Machine Learning and Electronic Health Records: A Systematic Review by Tianyi Liu, Andrew J. Krentz, Zhiqiang Huo, Vasa Ćurčin

“…The synthesis underscores the superiority of ML in modeling intricate EHR-derived risk factors, facilitating precision-driven cardiovascular risk assessment. …”
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Construction and validation of acetylation-related gene signatures for immune landscape analysis and prognostication risk prediction in luminal breast cancer by Mengdi Zhu, Jinna Lin, Haohan Liu, Jingru Wang, Nianqiu Liu, Yudong Li, Hongna Lai, Qianfeng Shi

“…Using Consensus Cluster Plus and the LASSO risk model, we screened 6 acetylation-related genes (KAT2B, TAF1L, CDC37, CCDC107, C17orf106, and ASPSCR1) and constructed a 6-gene risk model of luminal breast cancer. …”
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Investigating the Complexity of Multidimensional Symptom Experiences in Patients With Cancer: Systematic Review of the Network Analysis Approach by Vincent Richard, Allison Gilbert, Emanuela Pizzolla, Giovanni Briganti

“…Several NA approaches were used: network visualization (n=1), Bayesian network (n=1), pairwise Markov random field and IsingFit method (n=1), unregularized Gaussian graphical model (n=2), regularized partial correlation network (n=6), network visualization and community NA (n=1), network visualization and Walktrap algorithm (n=1), undirected network model with the Fruchterman-Reingold and edge-betweenness approaches (n=4), biased correlation and concise pattern diagram (n=1), extended Bayesian information criterion graphical LASSO method (n=3), cross-lagged panel network (n=1), and unspecified NA (n=3). …”
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TMSB4X is a regulator of inflammation-associated ferroptosis, and promotes the proliferation, migration and invasion of hepatocellular carcinoma cells by Linlin Tang, Yangli Jin, Jinxu Wang, Xiuyan Lu, Mengque Xu, Mingwei Xiang

“…Results 157 genes related to inflammation and ferroptosis in HCC were obtained by WGCNA. rLasso algorithm, with the highest C-index, screened out 29 hub genes, and this model showed good efficacy to predict the prognosis of HCC patients. …”
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Iterative phase contrast CT reconstruction with novel tomographic operator and data-driven prior. by Stefano van Gogh, Subhadip Mukherjee, Jinqiu Xu, Zhentian Wang, Michał Rawlik, Zsuzsanna Varga, Rima Alaifari, Carola-Bibiane Schönlieb, Marco Stampanoni

“…Moreover, the highly ill-conditioned differential nature of the GI-CT forward operator renders the inversion from corrupted data even more cumbersome. In this paper, we propose a novel regularized iterative reconstruction algorithm with an improved tomographic operator and a powerful data-driven regularizer to tackle this challenging inverse problem. …”
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Integrating status-neutral and targeted HIV testing in Zimbabwe: A complementary strategy. by Hamufare D Mugauri, Owen Mugurungi, Joconiah Chirenda, Kudakwashe Takarinda, Prosper Mangwiro, Mufuta Tshimanga

“…First tests were 65% more likely to test HIV positive (a95%CI: 1.43, 1.91) whilst screened patients were 3.89 times more likely to link to HIV prevention services (a95%CI: 3.05, 4.97), against 25.5% (n = 1,871) linkage among patients not screened.…”
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Novel insights of disulfidptosis-mediated immune microenvironment regulation in atherosclerosis based on bioinformatics analyses by Huanyi Zhao, Zheng Jin, Junlong Li, Junfeng Fang, Wei Wu, J. F. Fang

“…In addition, we established a foam cell model in vitro and an AS mouse model in vivo to verify the expressions of hub genes. …”
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Cadmium Exposure Disrupts Uterine Energy Metabolism and Coagulation Homeostasis During Labor in Institute of Cancer Research Mice: Insights from Transcriptomic Analysis by Yueyang Wang, Yichen Bai, Yi Wang, Yan Cai

“…This study is the first to establish a model of Cd exposure in the uterus of laboring mice and investigate the underlying metabolic mechanisms through transcriptomic analysis. …”
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Evaluation of three commercial rapid immunoassays for the diagnosis of Clostridioides difficile infection by Hannes Bjarki Vigfússon, Theresa Ennefors, Torbjörn Norén, Martin Sundqvist

“…The C. diff Quik Chek Complete performed the best of the three immunoassays, and when used in combination with NAAT, is a viable option for the laboratory diagnosis of CDI.IMPORTANCELaboratory diagnosis of Clostridioides difficile infection is complex, and current guidelines recommend a two-step diagnostic algorithm with a sensitive screening test and a more specific confirmatory test. …”
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Identification of specific metabolic pathways as druggable targets regulating the sensitivity to cyanide poisoning. by Patrick Y Sips, Xu Shi, Gabriel Musso, Anjali K Nath, Yanbin Zhao, Jason Nielson, Jordan Morningstar, Amy E Kelly, Brittney Mikell, Eva Buys, Vikhyat Bebarta, Jared Rutter, V Jo Davisson, Sari Mahon, Matthew Brenner, Gerry R Boss, Randall T Peterson, Robert E Gerszten, Calum A MacRae

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Antiviral therapy can effectively suppress irAEs in HBV positive hepatocellular carcinoma treated with ICIs: validation based on multi machine learning by Shuxian Pan, Zibing Wang

“…Predictive models were constructed using three machine learning algorithms to analyze and statistically evaluate clinical characteristics, including immune cell data. …”
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AI-Powered Synthesis of Structured Multimodal Breast Ultrasound Reports Integrating Radiologist Annotations and Deep Learning Analysis by Khadija Azhar, Byoung-Dai Lee, Shi Sub Byon, Kyu Ran Cho, Sung Eun Song

“…Our method synthesizes comprehensive breast US reports by combining the extracted information from radiologists’ annotations during routine screenings with the analysis results from deep learning algorithms on multimodal US images. …”
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