Construction of a stromal cell-related prognostic signature based on a 101-combination machine learning framework for predicting prognosis and immunotherapy response in triple-nega... by Fanrong Li, Congnan Jin, Yacheng Pan, Zheng Zhang, Liying Wang, Jieqiong Deng, Yifeng Zhou, Yifeng Zhou, Binbin Guo, Shenghua Zhang, Shenghua Zhang

“…A consensus MVP cell-related signature (MVPRS) was developed using 10 machine learning algorithms and 101 model combinations and validated in training and validation cohorts. …”
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Knowledge Distillation in Object Detection for Resource-Constrained Edge Computing by Arief Setyanto, Theopilus Bayu Sasongko, Muhammad Ainul Fikri, Dhani Ariatmanto, I. Made Artha Agastya, Rakandhiya Daanii Rachmanto, Affan Ardana, In Kee Kim

“…We compare various KD algorithms to identify the technique that produces a smaller model with the modest accuracy drop. …”
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A systematic comparison of short-term and long-term mortality prediction in acute myocardial infarction using machine learning models by Yawei Yang, Junjie Tang, Liping Ma, Feng Wu, Xiaoqing Guan

“…Mortality prediction was analysed using oversampling and feature selection methods coupled with machine learning algorithms. SHapley Additive exPlanations (SHAP) values were utilized to quantify the feature importance of AMI risk. …”
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Improved estimation of two-phase capillary pressure with nuclear magnetic resonance measurements via machine learning by Oriyomi Raheem, Misael M. Morales, Wen Pan, Carlos Torres-Verdín

“…In contrast, nuclear magnetic resonance (NMR) measurements, which provide information on pore body size distribution, are faster and can be leveraged to estimate capillary pressure using machine learning algorithms. Recently, artificial intelligence methods have also been applied to capillary pressure prediction (Qi et al., 2024).Currently, no readily applicable predictive model exists for estimating an entire capillary pressure curve directly from standard petrophysical logs and core data. …”
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Early Detection of Soil Salinization by Means of Spaceborne Hyperspectral Imagery by Giacomo Lazzeri, Robert Milewski, Saskia Foerster, Sandro Moretti, Sabine Chabrillat

“…Both datasets were pre-processed with multiple data transformation algorithms and 2D correlograms, PLSR and the Random Forest regressor were tested to identify the best model for salinity detection. …”
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Radiomic Analysis of Contrast‐Enhanced CT Predicts Recompensation in Hepatitis B‐Related Decompensated Cirrhosis by Qiaofeng Chen, Yuanping Fan, Kaini Wu, Tianpan Cai, Chunyu Lan, Mingju Yu, Yunfeng Fu, Qi Zhu, Jianhao Qiu, Xiaodong Zhou

“…Three machine‐learning algorithms were used to develop radiomic signatures. …”
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TEMPORALITY OF THE NARRATIVE OF CONTEMPORARY TRAVEL LITERATURE (Based on Cees Nooteboom’s work) by Olena I. Kretova, Pavlo V. Kretov

“…In the case of travel literature, the temporality of the narrative algorithmizes and structures the reception of reality, which is relevant even for such linguistic constructions that do not directly have markers of temporality, and the ability of such a narrative to configure and reconfigure formats of understanding and meanings of a work of art is emphasized both at the formal and at the content levels. …”
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Forestry climate adaptation with HarvesterSeasons service—a gradient boosting model to forecast soil water index SWI from a comprehensive set of predictors in Destination Earth by Mikko Strahlendorff, Anni Kröger, Golda Prakasam, Miriam Kosmale, Mikko Moisander, Heikki Ovaskainen, Asko Poikela

“…The Copernicus Global Land Monitoring Service’s Soil Water Index (SWI) satellite-based observations from 2015 to 2023 at 10,000 locations in Europe were used as the predictand (target parameter) to train an artificial intelligence (AI) model to predict soil wetness with XGBoost (eXtreme Gradient Boosting) and LightGBM (Light Gradient Boosting Machine) implementations of gradient boosting algorithms. The locations were selected as a representative set of points from the Land Use/Cover Area Frame Survey (LUCAS) sites, which helped evaluate the characteristics of distinct locations used in fitting to represent diverse landscapes across Europe. …”
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Epidemiological aspects of nephrolithiasis and chronic diseases of the cardiovascular system combination by R. V. Royuk, S. K. Yarovoy, N. A. Guseva, Sh. L. Voskanyan, V. V. Royuk, D. B. Rodin

“…The presence chronic cardiovascular diseases (CVD), especially complicated by congestive heart failure in patients with first time revealed nephrolithiasis, implies changes in the algorithms of metaphylactic of nephrolithiasis (regime of water loads, selection of diuretics and anticoagulants).…”
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Clinical characteristics, prognosis, and predictive modeling in class IV ± V lupus nephritis by Anjing Wang, Anjing Wang, Yunlong Qin, Yunlong Qin, Yan Xing, Zixian Yu, Liuyifei Huang, Jinguo Yuan, Yueqing Hui, Mei Han, Guoshuang Xu, Jin Zhao, Shiren Sun

“…The prognostic model was developed using machine learning algorithms and Cox regression. The model’s performance was evaluated in terms of discrimination, calibration, and risk classification using the concordance index (C-index), integrated brier score (IBS), net reclassification index (NRI), and integrated discrimination improvement (IDI), respectively.ResultsA total of 313 patients were enrolled for this study, including 156 class IV and 157 class IV+V LN. …”
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Mixed nontuberculous mycobacteria in an immunocompromised patient with progressive multifocal leukoencephalopathy by Dr Christoffel Opperman, Dr Janet Scott, Dr Aliasgar Esmail, Dr Sarishna Singh, Mr Yonas Ghebrekristos, Prof Rob warren, Dr Wynand Goosen

“…In this regard, next-generation sequencing and metagenomics have demonstrated superiority over DNA-DNA hybridization platforms (LPAs) in identifying NTM mixtures, suggesting their integration into diagnostic algorithms. The potential role of liposomal amikacin in mixed NTM disease warrants further investigation and could have been considered in this case. …”
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Construction and analysis of a prognostic risk scoring model for gastric cancer anoikis-related genes based on LASSO regression by Ai CHEN, Xiaowei CHEN, Yanan WANG, Xiaobing SHEN

“…Gene expression levels in gastric cancer clinical samples and cells were detected by real-time quantitative PCR (RT-qPCR); Kaplan-Meier (KM) survival curves, univariate and multivariate Cox regression analyses were used to verify the predictive efficiency of the prognostic risk scoring model for the prognosis of gastric cancer patients; CIBERSORT and ESTIMATE algorithms were used to analyze the immune cell infiltration levels in patients with different risk groups; the correlation between risk scores and immune checkpoint expression levels in gastric cancer patients was analyzed using the R package "ggplot2" and "ggExtra", and the correlation between tumor mutation burden (TMB) and risk scores was assessed; chemotherapy drug sensitivity analysis was used to evaluate the value of the constructed prognostic risk scoring model in gastric cancer chemotherapy. …”
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Pharmacological potential of 3,5-dimethyl-4-(3-(5-nitrofuran-2-yl)allylidenamino)-1-alkyl-1,2,4-triazolium bromides by A. S. Hotsulia, O. I. Panasenko, T. S. Brytanova

“…A molecular docking method that uses a variety of computational algorithms to predict and analyze interactions, including determining the presence of possible binding sites, estimating binding energies, and the spatial arrangement of molecules. …”
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Molecular subtype of recurrent implantation failure reveals distinct endometrial etiology of female infertility by Jing Yang, Lingtao Yang, Ying Zhou, Fengyang Cao, Hongkun Fang, Huan Ma, Jun Ren, Chunyu Huang, Lianghui Diao, Qiyuan Li, Qionghua Chen

“…A molecular classifier (MetaRIF) was developed using the optimal F-score from 64 combinations of machine learning algorithms. Candidate therapeutic compounds were predicted using the Connectivity Map (CMap) database. …”
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Diagnostic accuracy of artificial intelligence for the screening of prostate cancer in biparametric magnetic resonance imaging: a systematic review by Oksana V. Kryuchkova, Elena V. Schepkina, Natalia A. Rubtsova, Boris Y. Alekseev, Anton I. Kuznetsov, Svetlana V. Epifanova, Elena V. Zarya, Ali E. Talyshinskii

“…The most common machine-learning algorithms applied by the investigators were as follows: multiple logistic regression (76%), support vector machine (38%), and random forest (24%). …”
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Editorial by Bulent Cavas

“…The eighth article, “Prediction of Middle School Students’ Recycling Behaviors with Machine Learning Algorithms,” by Mustafaoğlu and Alkan (Türkiye), applies machine learning techniques to identify factors predicting students' recycling behavior. …”
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An artificial intelligence model to predict mortality among hemodialysis patients: A retrospective validated cohort study by Zhong Peng, Shuzhu Zhong, Xinyun Li, Fengyi Yu, Zixu Tang, Chunyuan Ma, Zihao Liao, Song Zhao, Yuan Xia, Haojun Fu, Wei Long, Mingxing Lei, Zhangxiu He

“…The machine learning algorithms used to develop the models for the training group included logistic regression (LR), decision tree (DT), extreme gradient boosting machine (eXGBM), neural network (NN), and support vector machine (SVM). …”
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Emerging Diagnostic Approaches for Musculoskeletal Disorders: Advances in Imaging, Biomarkers, and Clinical Assessment by Rahul Kumar, Kiran Marla, Kyle Sporn, Phani Paladugu, Akshay Khanna, Chirag Gowda, Alex Ngo, Ethan Waisberg, Ram Jagadeesan, Alireza Tavakkoli

“…Artificial intelligence and machine learning algorithms now automate image interpretation, predict clinical outcomes, and enhance clinical decision support, complementing conventional clinical evaluations. …”
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Efficiency and safety of the Russian-made KERATOLINK device used to treat patients with stage I–II keratoconus and pellucid marginal corneal degeneration by A. T. Khandzhyan, E. N. Iomdina, A. V. Ivanova, A. S. Sklyarova, N. V. Khodzhabekyan, I. V. Manukyan

“…The use of local UVCL and accelerated algorithms with a significant reduced exposure time improves the comfort and tolerability of the procedure, and also reduces the risk of complications. …”
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Characterization of severity of hemolytic disease of the fetus and newborn due to Rhesus antigen alloimmunizationAJOG Global Reports at a Glance by Alexis A. Krumme, ScD, Robert Y. Suruki, ScD, Clair Blacketer, MPH, Jill Hardin, PhD, Joel N. Swerdel, PhD, May Lee Tjoa, PhD, Kara B. Markham, MD

“…Neonatal and maternal records were linked algorithmically by shared family identifier. A hierarchical severity index was developed for neonates with a Rhesus antigen hemolytic disease of the fetus and newborn diagnosis code in the first 30 days of life, using antenatal and neonatal diagnoses and treatments: Severe (neonatal death, hydrops fetalis, intrauterine transfusion); Moderate (neonatal exchange transfusion); Mild (neonatal simple transfusion); Minimal (neonatal phototherapy or hyperbilirubinemia). …”
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