Non-invasive urinary proteomic biomarkers for prognostic assessment in sepsis by Qingbo Zeng, Nianqing Zhang, Junjie Zeng, Lincui Zhong, Qingwei Lin, Longping He, Xiaomin Song, Jingchun Song

“…Feature variables were selected from the differentially expressed proteins using the Least Absolute Shrinkage and Selection Operator (LASSO) and the random forest algorithms and by determining whether their proteins had an area under the curve (AUC) greater than 0.8. …”
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Mapping Gridded GDP Distribution of China Based on Remote Sensing Data and Machine Learning Methods by Saimiao Liu, Wenliang Liu, Yi Zhou, Shixin Wang, Futao Wang, Zhenqing Wang

“…Therefore, based on the remote sensing data of land use and nighttime light, this study developed two methods: the factor averaging method (FAM) and grid averaging method (GAM), and used Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) algorithms to jointly construct the spatial model of GDP, so as to produce China’s 1 km gridded GDP in 2020. …”
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AI in dermatology: a comprehensive review into skin cancer detection by Kavita Behara, Ernest Bhero, John Terhile Agee

“…Results AI-based models exhibit remarkable performance in skin cancer detection by leveraging advanced deep learning algorithms, image processing techniques, and feature extraction methods. …”
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Identification of a Prognosis-Related Risk Signature for Bladder Cancer to Predict Survival and Immune Landscapes by Linhui Wang, Yutao Wang, Jianfeng Wang, Luanfeng Li, Jianbin Bi

“…The potential biological functions of the selected genes were analyzed using CIBERSORT and ESTIMATE algorithms. Cancer Therapeutics Response Portal (CTRP) and PRISM datasets were used to identify drugs with high sensitivity. …”
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Analysis and Validation of Autophagy-Related Gene Biomarkers and Immune Cell Infiltration Characteristic in Bronchopulmonary Dysplasia by Integrating Bioinformatics and Machine Lea... by Xiao S, Ding Y, Du C, Lv Y, Yang S, Zheng Q, Wang Z, Zheng Q, Huang M, Xiao Q, Ren Z, Bi G, Yang J

“…Subsequently, the hub genes were identified by Lasso and Cytoscape with three machine-learning algorithms (MCC, Degree and MCODE). In addition, hub genes were validated with ROC, single-cell sequence and IHC in hyperoxia mice. …”
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MINIMIZATION OF RISK OF THE ERRONEOUS DECISION IN THE ASSESSMENT OF THE IMPORTANCE OF STATISTICAL RELATIONS OF TECHNICAL AND ECONOMIC INDICATORS OF THE OBJECTS OF ELECTRIC POWER SY... by E. M. Farhadzadeh, A. Z. Muradaliyev, Yu. Z. Farzaliyev, T. K. Rafiyeva, S. A. Abdullayeva

“…The novelty consists in the application of fiducial approach; the calculation of critical values are fulfilled with the aid of computer technologies of simulation of possible realizations of the correlation coefficients for the two assumptions, viz. technical and economic indicators of the independent and dependent; simulation is fulfilled with the method of solving the “inverse problem”, which enables the possible implementation of the correlation coefficients for the really dependent and independent samples of random variables at a given sample size; the developed algorithms and programs for calculation made it possible to obtain the critical values of correlation coefficients for independent and dependent samples; in conditions of the sameness of the consequences of erroneous decisions it is proposed to make a decision not based on critical value but based on the boundary values of the correlation coefficients that correspond to the minimum total risk of erroneous decisions; the exemplification of the recommendations application was made on example of technical and economic parameters of boilers of power units of 300 MWt. …”
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Computed tomography-based radiomics model for predicting station 4 lymph node metastasis in non-small cell lung cancer by Yanru Kang, Mei Li, Xizi Xing, Kaixuan Qian, Hongxia Liu, Yafei Qi, Yanguo Liu, Yi Cui, Hua Zhang

“…Four machine learning algorithms—decision tree (DT), logistic regression (LR), random forest (RF), and support vector machine (SVM)—were employed to construct radiomics models. …”
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Di George syndrome: early diagnosis in pediatric practice by A. A. Pavlikov, I. M. Melnikova, T. I. Pakhomova, N. P. Myakin

“…Due to the fact that an increasing number of patients with 22q11.2DS survive to adulthood, the development of algorithms for the prognosis of this disease is important.…”
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Optimizing radiomics for prostate cancer diagnosis: feature selection strategies, machine learning classifiers, and MRI sequences by Eugenia Mylona, Dimitrios I. Zaridis, Charalampos Ν. Kalantzopoulos, Nikolaos S. Tachos, Daniele Regge, Nikolaos Papanikolaou, Manolis Tsiknakis, Kostas Marias, ProCAncer-I Consortium, Dimitrios I. Fotiadis

“…Feature selection method impacts radiomics models’ performance more than ML algorithms. Best feature selection methods: RFE, LASSO, RF, and Boruta. …”
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Integrated WHT- and DFT-Based Multiuser Massive MIMO System for Secure RIS-Enabled mmWave Communications by Md. Najmul Hossain, Md. Rakibul Islam, S. K. Tamanna Kamal, Atia Kaniz, Shaikh Enayet Ullah, Tetsuya Shimamura

“…BD precoding minimizes multiuser interference, and using integrated WHT- and DFT-based algorithms with null carriers effectively reduces the out-of-band (OOB) spectrum power. …”
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CRISP: A causal relationships-guided deep learning framework for advanced ICU mortality prediction by Linna Wang, Xinyu Guo, Haoyue Shi, Yuehang Ma, Han Bao, Lihua Jiang, Li Zhao, Ziliang Feng, Tao Zhu, Li Lu

“…Conclusion CRISP achieves better generalizability across different patient groups, compared to various baseline algorithms, thereby enhancing the practical application of DL in clinical decision support. …”
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Accuracy Evaluation of Multi-Source Precipitation Data in Mountain Flood Simulation in Guoning Village， Xiahe County by Wubin HUANG, Jing FU, Runxia GUO, Junxia ZHANG, Yu LEI

“…From 22:00 on September 6， 2023 to 04:00 （Beijing Time） on September 7， Xiahe County in Gansu Province experienced severe convective weather， with short-term heavy rainfall in some areas， causing flash floods in Guoning Village， Xiahe County， resulting in casualties.In this study， the characteristics of Radar Quantitative Precipitation Estimation （Radar-QPE）， FengYun 4B Quantitative Precipitation Estimation （FY4B-QPE）， and CMA Multi-source Precipitation Analysis （CMPA） precipitation products were contrastive analyzed based on meteorological station observations.These precipitation data were used to drive the hydrodynamic hydrological model and evaluate the effect of different precipitation data in the flash flood simulation.The results showed that： （1） Among the 12-hour cumulative precipitation amounts， CMPA demonstrated higher accuracy in terms of the position of large value areas and differences in local precipitation levels； Radar-QPE was closer to AWS （Automatic Weather Station） in terms of cumulative precipitation level but showed significant differences in spatial distribution； FY4B-QPE overestimated the cumulative precipitation level by 33.8%.（2） In terms of hourly distribution， CMPA was most similar to AWS in terms of temporal evolution， spatial distribution， and precipitation level； Radar-QPE's peak values were smaller， and the peak times were lagged， with negative deviations in precipitation being dominant； FY4B-QPE's peak values and peak times were consistent with reality， but there were deviations in the start and end times of precipitation， with positive deviations in precipitation being dominant.（3） In the hydrological simulation study， CMPA， Radar-QPE， and FY4B-QPE all overestimated water levels， but the timing of water level peaks was more consistent with AWS.CMPA performed best in terms of RMSE （Root Mean Square Error）， NSE （Nash Efficiency Coefficient）， and Bias （Relative Deviation）， followed by Radar-QPE， and FY4B-QPE performed relatively poorly.Although existing site-observed precipitation cannot fully meet the needs of research and early warning for small and medium scale mountain floods， the high precision of CMPA data could effectively supplement the deficiencies of traditional meteorological observation stations to some extent.Meanwhile， the algorithms and accuracy of Radar-QPE and FY4B-QPE needed to be further improved and enhanced.…”
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NDVI estimation using Sentinel-1 data over wheat fields in a semiarid Mediterranean region by Emna Ayari, Zeineb Kassouk, Zohra Lili-Chabaane, Nadia Ouaadi, Nicolas Baghdadi, Mehrez Zribi

“…Relative low accuracy characterizes the regression algorithms’ estimations when NDVI ≥ 0.4 compared to their performance during the aforementioned periods. …”
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IoT-driven smart agricultural technology for real-time soil and crop optimization by Hammad Shahab, Muhammad Naeem, Muhammad Iqbal, Muhammad Aqeel, Syed Sajid Ullah

“…By integrating advanced IoT technologies, cloud computing, predictive algorithms, and a smart soil sensor, this system revolutionizes agriculture by enabling real-time monitoring of critical factors influencing rice crops metabolism. …”
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Cardiovascular Disease Screening in Primary School Children by Alena Bagkaki, Fragiskos Parthenakis, Gregory Chlouverakis, Emmanouil Galanakis, Ioannis Germanakis

“…Following expert verification of responses and obtained data, assisted by designated electronic health record with incorporated decision support algorithms (phase II), pediatric cardiology evaluation at the tertiary referral center followed (phase III). …”
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Development of a PPP1R14B-associated immune prognostic model for hepatocellular carcinoma by Ligang Zhao, Xiaoling Jin, Qiushi Yu, Yigang He, Facai Yang, Weihu Ma, Zhengqing Lei, Jiahua Zhou

“…The immune landscape was assessed utilizing CIBERSORT and ESTIMATE algorithms. Gene set variation examination helped explore immune cell populations and their activities. …”
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Analisis Buku “Mari Belajar Matematika” Karya Dewi Nuharini dan Sulis Priyanto Tahun 2017 by Arum Weni, Ryky Mandar Sary, Veryliana Purnamasari

“…The results of the analysis that has been carried out are the percentage details for each indicator as follows. (1) Materials containing concepts, definitions, procedures/algorithms, theorems and properties get 75% percentage in the appropriate category. (2) The material contains examples and practice questions that support the concept of getting a percentage of 100% with a very appropriate category. (3) The material contains explanations that can build students' knowledge and gets a percentage of 100% with a very appropriate category. …”
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Identification of diagnostic biomarkers and dissecting immune microenvironment with crosstalk genes in the POAG and COVID-19 nexus by Changfan Peng, Long Hu, Wanwen Su, Xin Hu

“…Concurrently, gene expression datasets from GEO (POAG: GSE27276; COVID-19: GSE171110, GSE152418) were used to identify 57 crosstalk genes (CGs) via differential expression analysis. Machine learning algorithms (LASSO, SVM-RFE, Random Forest) were applied to screen POAG diagnostic biomarkers from CGs, followed by construction of transcription factor (TF)-microRNA (miRNA)-protein-compound regulatory networks and consensus clustering to characterize COVID-19 immune microenvironment subtypes. …”
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Multimorbidity of cardiometabolic diseases: a cross-sectional study of patterns, clusters and associated risk factors in sub-Saharan Africa by Charles Agyemang, Frederick Wekesah, Calistus Wilunda, Gershim Asiki, Peter Otieno, Richard E Sanya, Welcome Wami

“…Objective To determine the patterns of cardiometabolic multimorbidity and associated risk factors in sub-Saharan Africa (SSA).Design We used data from the WHO STEPwise approach to non-communicable disease risk factor surveillance cross-sectional surveys conducted between 2014 and 2017.Participants The participants comprised 39, 658 respondents aged 15–69 years randomly selected from nine SSA countries using a multistage stratified sampling design.Primary outcome measure Using latent class analysis and agglomerative hierarchical clustering algorithms, we analysed the clustering of cardiometabolic diseases (CMDs) including high blood sugar, hypercholesterolaemia, hypertension and cardiovascular diseases (CVDs) such as heart attack, angina and stroke. …”
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Machine Learning-Driven Prediction of One-Year Readmission in HFrEF Patients: The Key Role of Inflammation by Ma F, Hu Y, Han P, Qiu Y, Liu Y, Ren J

“…Seven machine learning (ML) algorithms were trained and validated using a 7:3 dataset split; the metrics of the model included the area under the curve (AUC), accuracy, sensitivity, specificity, F1 score, and Brier score. …”
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