Experimental validation of cuproptosis-associated molecular signatures and their immunological implications in pulmonary tuberculosis by Xiaofang Liu, Xiaofang Liu, Qianqian Ma, Zhiming Li, Yong Xue, Jie Mi, Yuxi Li, Chunfeng Bai, Donglin Guo, Yinping Liu, Yan Liang, Jianqin Liang, Xueqiong Wu

“…Hub CRGs were screened out via least absolute shrinkage and selection operator (LASSO) regression and random forest (RF) algorithms. Diagnostic models were subsequently constructed and validated. …”
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Development of a robust FT-IR typing system for Salmonella enterica, enhancing performance through hierarchical classification by Diego Fredes-García, Javiera Jiménez-Rodríguez, Alejandro Piña-Iturbe, Pablo Caballero-Díaz, Tamara González-Villarroel, Fernando Dueñas, Aniela Wozniak, Aiko D. Adell, Andrea I. Moreno-Switt, Patricia García

“…The IR Biotyper was used to acquire spectra from these isolates. Machine learning algorithms, including support vector machines, were trained to classify the isolates. …”
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Pharmacological potential of 6,11-dihydro[1,2,4]triazolo[4’,3’:1,6]-pyrido[3,4-b]-5-carboxylic acid and its esters by S. O. Fedotov, A. S. Hotsulia

“…The molecular docking method, based on computational algorithms, has been used to predict and study interactions, including the identification of potential binding sites, estimation of energy parameters and spatial configuration of molecules. …”
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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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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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The role of artificial intelligence in managing hospitalized patients with mental illness: a scoping review by Md Samiun, Moustaq Karim Khan Rony, Sumaiya Yeasmin, Mia Md Tofayel Gonee Manik, Anupom Debnath, Mustakim Bin Aziz, Afia Fairooz Tasnim, Sadia Islam Nilima

“…Despite these advancements, challenges were noted, such as algorithmic biases due to non-diverse training data, privacy and security risks for sensitive patient information, and clinician resistance stemming from concerns about autonomy and ethical implications of AI-supported clinical decisions. …”
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Towards accurate L4 ocean colour products: Interpolating remote sensing reflectance via DINEOF by Christian Marchese, Simone Colella, Vittorio Ernesto Brando, Maria Laura Zoffoli, Gianluca Volpe

“…Our outcomes show that this “upstream interpolation” method can generate a consistent Rrs dataset, thereby improving the accuracy of L4 Chl predictions when used as input in algorithms for remote Chl estimation. …”
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Automatic Selection of Machine Learning Models for Armed People Identification by Alonso Javier Amado-Garfias, Santiago Enrique Conant-Pablos, Jose Carlos Ortiz-Bayliss, Hugo Terashima-Marin

“…We developed a series of algorithms to create a dataset with the information extracted from the bounding boxes generated by YOLOv4 in real-time. …”
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Leveraging Artificial Intelligence and Data Science for Integration of Social Determinants of Health in Emergency Medicine: Scoping Review by Ethan E Abbott, Donald Apakama, Lynne D Richardson, Lili Chan, Girish N Nadkarni

“…With a significant focus on the ED and notable NLP model performance, there is an imperative to standardize SDOH data collection, refine algorithms for diverse patient groups, and champion interdisciplinary synergies. …”
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Development and Testing a New Online Dynamic Nomogram for Contrast-Induced Acute Kidney Injury in Elderly Patients with ST-Segment Elevation Myocardial Infarction by Jin J, Ding J, Zhang X, Wang L, Zhang X, Li W, Li S

“…Lasso regression selected predictors, and nine Machine Learning (ML) algorithms were evaluated via Receiver Operating Characteristic (ROC) analysis. …”
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Quality Assessment of MRI-Radiomics-Based Machine Learning Methods in Classification of Brain Tumors: Systematic Review by Shailesh S. Nayak, Saikiran Pendem, Girish R. Menon, Niranjana Sampathila, Prakashini Koteshwar

“…Various imaging modalities, including MRI, PET/CT, and advanced techniques like ASL and DTI, were utilized to extract radiomic features for analysis. Machine learning algorithms such as deep learning networks, support vector machines, random forests, and logistic regression were applied to develop predictive models. …”
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Critical gene network and signaling pathway analysis of the extracellular signal-regulated kinase (ERK) pathway in ischemic stroke by Rui Mao, Lei Wang, Haitao Zhang, Jiaojiao Gong, Hua Liu

“…Four machine learning algorithms (Boruta, SVM, LASSO, random forest) corroborated hub gene robustness. …”
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Nasolacrimal Duct Obstruction Secondary to Radioactive Iodine-131 Therapy for Differentiated Thyroid Cancer by A. A. Trukhin, V. D. Yartsev, M. S. Sheremeta, D. V. Yudakov, M. O. Korchagina, R. Kh. Salimkhanov, S. V. Grishkov

“…The authors have developed a new method for predicting secondary NLDO by a combination of the patient’s individual parameters and treatment plan; the identified predictors help to personalise radioiodine therapy. …”
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Identification of potential pathogenic genes associated with the comorbidity of rheumatoid arthritis and renal fibrosis using bioinformatics and machine learning by Jiao Qiu, Yalin Xu, Luyuan Tong, Xingchun Yang, Xiao Wu

“…A LR model was developed, and its predictive accuracy was verified using the ROC curve derived from an external independent dataset. …”
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MMLT: Efficient object tracking through machine learning-based meta-learning by Bibek Das, Asfak Ali, Suvojit Acharjee, Jaroslav Frnda, Sheli Sinha Chaudhuri

“…While Deep learning algorithms address these challenges, however, they typically require significant computational resources, exhibit high complexity, and demand large amounts of training data. …”
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Integrative Analysis of Neutrophil-Associated Genes Reveals Prognostic Significance and Immune Microenvironment Modulation in Cervical Cancer by Ting Hu, Haijing Wu, Xinghan Cheng, Haoyue Gao, Min Yang

“…High-risk patients exhibited an immunosuppressive tumor microenvironment, elevated TIDE scores, and lower predicted responsiveness to immunotherapy. SEMA6B was significantly downregulated in the tumour group but may be reactivated during metastasis. …”
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Agricultural Machinery Path Tracking with Varying Curvatures Based on an Improved Pure-Pursuit Method by Jiawei Zhou, Junhao Wen, Liwen Yao, Zidong Yang, Lijun Xu, Lijian Yao

“…The current research on path tracking primarily focuses on improving control algorithms, such as adaptive and predictive models, to enhance tracking accuracy and stability. …”
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USE OF METHODOLOGICAL TASKS IN PROFESSIONAL TRAINING OF FUTURE TEACHERS OF INFORMATICS by Nataliia S. Pavlova

“…The methodological task integrates the main elements of methodological activity concerning planning and designing of educational process and its management by the teacher, and also predicts educational and cognitive actions of pupils. …”
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Develoment of The Computer Simulation of Oscillation in Physics Learning by Y Sumardi, A F Amalia, U N Prabowo

“…The use of simulations within teaching process had a positive impact on students’ being able to predict and explain the phenomena. It also designed to provide possibility to explore physics phenomenon which cannot be realized in school conditions.  …”
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Spatial-Spectral Linear Extrapolation for Cross-Scene Hyperspectral Image Classification by Lianlei Lin, Hanqing Zhao, Sheng Gao, Junkai Wang, Zongwei Zhang

“…Popular DG strategies constrain the model’s predictive behavior in synthetic space through deep, nonlinear source expansion, and an HSI generation model is usually adopted to enrich the diversity of training samples. …”
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