In Silico Identification of New Anti-SARS-CoV-2 Main Protease (Mpro) Molecules with Pharmacokinetic Properties from Natural Sources Using Molecular Dynamics (MD) Simulations and Hi... by Harrison Onyango, Patrick Odhiambo, David Angwenyi, Patrick Okoth

“…In this study, computational algorithms were utilized for virtual screening of a library of natural compounds in the ZINC database for their affinity towards SARS-CoV-2 Mpro. …”
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Inhibition of complement system-related gene ITGB2 attenuates epithelial–mesenchymal transition and inflammation in diabetic nephropathy by Jun Peng, Wenqi Zhao, Lu Zhou, Kun Ding

“…In addition, key biomarkers were acquired by two machine learning algorithms, then immune infiltration analysis, Gene Set Enrichment Analysis (GSEA), and potential drugs screening were conducted. …”
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The systemic oxidative stress index predicts clinical outcomes of esophageal squamous cell carcinoma receiving neoadjuvant immunochemotherapy by Jifeng Feng, Jifeng Feng, Liang Wang, Xun Yang, Qixun Chen, Qixun Chen

“…Then, a new staging that included TNM and SOSI based on RPA algorithms was produced. In terms of prognostication, the RPA model performed significantly better than TNM classification.ConclusionSOSI is a simple and useful score based on available SOS-related indices. …”
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Applied machine learning for predicting the properties and carbon and phosphorus fate of pristine and engineered hydrochar by Shiyu Xie, Tao Zhang, Siming You, Santanu Mukherjee, Mingjun Pu, Qing Chen, Yaosheng Wang, Esmat F. Ali, Hamada Abdelrahman, Jörg Rinklebe, Sang Soo Lee, Sabry M. Shaheen

“…Also, different ML algorithms were used to model and predict the hydrochar solid yield, properties, and nutrients content. …”
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Machine-Learning Parsimonious Prediction Model for Diagnostic Screening of Severe Hematological Adverse Events in Cancer Patients Treated with PD-1/PD-L1 Inhibitors: Retrospective... by Seok Jun Park, Seungwon Yang, Suhyun Lee, Sung Hwan Joo, Taemin Park, Dong Hyun Kim, Hyeonji Kim, Soyun Park, Jung-Tae Kim, Won Gun Kwack, Sung Wook Kang, Yun-Kyoung Song, Jae Myung Cha, Sang Youl Rhee, Eun Kyoung Chung

“…Among the tested ML algorithms, random forest achieved the highest accuracy (area under the receiver operating characteristic curve [AUROC] 0.88 for both cohorts). …”
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Multilevel determinants of racial/ethnic disparities in severe maternal morbidity and mortality in the context of the COVID-19 pandemic in the USA: protocol for a concurrent triang... by Bankole Olatosi, Jiajia Zhang, Xiaoming Li, Chen Liang, Jihong Liu, Peiyin Hung, Shan Qiao, Berry A Campbell, Myriam E Torres, Neset Hikmet

“…Non-Hispanic black and Hispanic pregnant women appear to have disproportionate SARS-CoV-2 infection and death rates.Methods and analysis We will use the socioecological framework and employ a concurrent triangulation, mixed-methods study design to achieve three specific aims: (1) examine the impacts of the COVID-19 pandemic on racial/ethnic disparities in severe maternal morbidity and mortality (SMMM); (2) explore how social contexts (eg, racial/ethnic residential segregation) have contributed to the widening of racial/ethnic disparities in SMMM during the pandemic and identify distinct mediating pathways through maternity care and mental health; and (3) determine the role of social contextual factors on racial/ethnic disparities in pregnancy-related morbidities using machine learning algorithms. We will leverage an existing South Carolina COVID-19 Cohort by creating a pregnancy cohort that links COVID-19 testing data, electronic health records (EHRs), vital records data, healthcare utilisation data and billing data for all births in South Carolina (SC) between 2018 and 2021 (>200 000 births). …”
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MASFNet: Multi-level attention and spatial sampling fusion network for pine wilt disease trees detection by Dong Ren, Meng Li, Ziyu Hong, Li Liu, Jingfeng Huang, Hang Sun, Shun Ren, Pan Sao, Wenbin Wang, Jingcheng Zhang

“…However, due to the diversity of object information in UAV remote sensing images, most existing algorithms are prone to confusing the background environment and difficult to distinguish highly similar ground objects, resulting in a lot of false detections. …”
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Predicting the risk of heart failure after acute myocardial infarction using an interpretable machine learning model by Qingqing Lin, Qingqing Lin, Wenxiang Zhao, Wenxiang Zhao, Hailin Zhang, Hailin Zhang, Wenhao Chen, Sheng Lian, Qinyun Ruan, Qinyun Ruan, Zhaoyang Qu, Zhaoyang Qu, Yimin Lin, Yimin Lin, Dajun Chai, Dajun Chai, Dajun Chai, Dajun Chai, Xiaoyan Lin, Xiaoyan Lin, Xiaoyan Lin, Xiaoyan Lin

“…For developing a predictive model for HF risk in AMI patients, the least absolute shrinkage and selection operator (LASSO) Regression was used to feature selection, and four ML algorithms including Random Forest (RF), Extreme Gradient Boost (XGBoost), Support Vector Machine (SVM), and Logistic Regression (LR) were employed to develop the model on the training set. …”
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A novel gene signature for predicting outcome in colorectal cancer patients based on tumor cell-endothelial cell interaction via single-cell sequencing and machine learning by Lina Pang, Qingxia Sun, Wenyue Wang, Mingjie Song, Ying Wu, Xin Shi, Xiaonan Shi

“…Prognostic signatures were developed using various machine learning algorithms based on marker genes linked to the identified cell subpopulations. …”
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Identification and validation of glycolysis-related diagnostic signatures in diabetic nephropathy: a study based on integrative machine learning and single-cell sequence by Xiaoyin Wu, Xiaoyin Wu, Buyu Guo, Buyu Guo, Xingyu Chang, Xingyu Chang, Yuxuan Yang, Yuxuan Yang, Qianqian Liu, Qianqian Liu, Jiahui Liu, Jiahui Liu, Yichen Yang, Yichen Yang, Kang Zhang, Yumei Ma, Songbo Fu, Songbo Fu, Songbo Fu

“…The expression levels of diagnostic signatures were verified in vitro.ResultsThrough the 108 combinations of machine learning algorithms, we selected 12 diagnostic signatures, including CD163, CYBB, ELF3, FCN1, PROM1, GPR65, LCN2, LTF, S100A4, SOX4, TGFB1 and TNFAIP8. …”
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Serum metabolome associated with novel and legacy per- and polyfluoroalkyl substances exposure and thyroid cancer risk: A multi-module integrated analysis based on machine learning by Fei Wang, Yuanxin Lin, Lian Qin, Xiangtai Zeng, Hancheng Jiang, Yanlan Liang, Shifeng Wen, Xiangzhi Li, Shiping Huang, Chunxiang Li, Xiaoyu Luo, Xiaobo Yang

“…PFHxA and PFDoA exposure associated with increased TC risk, while PFHxS and PFOA associated with decreased TC risk in single compound models (all P < 0.05). Machine learning algorithms identified PFHxA, PFDoA, PFHxS, PFOA, and PFHpA as the key PFAS influencing the development of TC, and mixed exposures have an overall positive effect on TC risk, with PFHxA making the primary contribution. …”
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Enhancing individual glomerular filtration rate assessment: can we trust the equation? Development and validation of machine learning models to assess the trustworthiness of estima... by Antoine Lanot, Anna Akesson, Felipe Kenji Nakano, Celine Vens, Jonas Björk, Ulf Nyman, Anders Grubb, Per-Ola Sundin, Björn O. Eriksen, Toralf Melsom, Andrew D. Rule, Ulla Berg, Karin Littmann, Kajsa Åsling-Monemi, Magnus Hansson, Anders Larsson, Marie Courbebaisse, Laurence Dubourg, Lionel Couzi, Francois Gaillard, Cyril Garrouste, Lola Jacquemont, Nassim Kamar, Christophe Legendre, Lionel Rostaing, Natalie Ebert, Elke Schaeffner, Arend Bökenkamp, Christophe Mariat, Hans Pottel, Pierre Delanaye

“…Four machine learning and two traditional logistic regression models were trained on a cohort of 9,202 participants to predict the likelihood of the EKFC creatinine-derived eGFR falling within 30% (p30), 20% (p20) or 10% (p10) of the mGFR value. The algorithms were internally and then externally validated on cohorts of respectively 3,034 and 10,107 participants. …”
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Analysis of mutations in CDC27, CTBP2, HYDIN and KMT5A genes in carotid paragangliomas by E. N. Lukyanova, A. V. Snezhkina, D. V. Kalinin, A. V. Pokrovsky, A. L. Golovyuk, O. A. Stepanov, E. A. Pudova, G. S. Razmakhaev, M. V. Orlova, A. P. Polyakov, M. V. Kiseleva, A. D. Kaprin, A. V. Kudryavtseva

“…Using several prediction algorithms (SIFT, PolyPhen-2, MutationTaster, and LRT), potentially pathogenic mutations were identified in four genes (CDC27, CTBP2, HYDIN, and KMT5A). …”
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Deciphering the role of metal ion transport-related genes in T2D pathogenesis and immune cell infiltration via scRNA-seq and machine learning by Zuhui Pu, Zuhui Pu, Tony Bowei Wang, Ying Lu, Ying Lu, Zijing Wu, Zijing Wu, Yuxian Chen, Ziqi Luo, Xinyu Wang, Lisha Mou, Lisha Mou

“…We employed 12 machine learning algorithms to develop predictive models and assessed immune cell infiltration using single-sample gene set enrichment analysis (ssGSEA). …”
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Children with autoimmune hepatitis receiving standard-of-care therapy demonstrate long-term obesity and linear growth delay by Or Steg Saban, Shannon M. Vandriel, Syeda Aiman Fatima, Celine Bourdon, Amrita Mundh, Vicky L. Ng, Simon C. Ling, Robert H.J. Bandsma, Binita M. Kamath

“…These data indicate the need to re-evaluate standard treatment algorithms for pediatric AIH in terms of steroid dosing and potential nonsteroid alternatives.…”
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The association of origin and environmental conditions with performance in professional IRONMAN triathletes by Beat Knechtle, Mabliny Thuany, David Valero, Elias Villiger, Pantelis T. Nikolaidis, Marilia S. Andrade, Ivan Cuk, Thomas Rosemann, Katja Weiss

“…Three different ML models were built and evaluated, based on three algorithms, in order of growing complexity and predictive power: Decision Tree Regressor, Random Forest Regressor, and XG Boost Regressor. …”
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Low-cost phone-based LiDAR scanning technology provides sub-centimeter accuracy when measuring the main dimensions of motor-manual tree felling cuts by Stelian Alexandru Borz, Andrea Rosario Proto

“…Short-range LiDAR technology integrated in affordable mobile platforms has already been proved to produce reliable estimates on objects located in a limited space, and point cloud processing algorithms have been developed to compare two instances of the same object, potentially enabling the quantification of tree-level wood loss. …”
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Relationship between stress hyperglycemia ratio and progression of non target coronary lesions: a retrospective cohort study by Shiqi Liu, Ziyang Wu, Gaoliang Yan, Yong Qiao, Yuhan Qin, Dong Wang, Chengchun Tang

“…Logistic regression models, restricted cubic spline analysis, and machine learning algorithms (LightGBM, decision tree, and XGBoost) were utilized to analyse the relationship of stress hyperglycemia ratio and non target lesion progression. …”
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&#x03F5;-Confidence Approximately Correct (&#x03F5;-CoAC) Learnability and Hyperparameter Selection in Linear Regression Modeling by Soosan Beheshti, Mahdi Shamsi

“…Linear regression modeling is an important category of learning algorithms. The practical uncertainty of the label samples in the training data set has a major effect in the generalization ability of the learned model. …”
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Experimental study and model prediction of the influence of different factors on the mechanical properties of saline clay by Hui Cheng, Lingkai Zhang, Chong Shi, Pei Pei Fan

“…The boundary point of the 2% salt content divides the effect of salt ions from promoting free water flow to blocking seepage channels, with the proportion of micropores being the primary influencing factor. (4) Employing statistical theory and machine learning algorithms, dry density, water content, and salinity are used to predict mechanical index values. …”
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