Exploring the Key Features of Repeating Fast Radio Bursts with Machine Learning by Wan-Peng Sun, Ji-Guo Zhang, Yichao Li, Wan-Ting Hou, Fu-Wen Zhang, Jing-Fei Zhang, Xin Zhang

“…However, there are potentially a number of nonrepeaters that may be misclassified, as repeating bursts are missed due to the limited sensitivity and observation periods, thus misleading the investigation of their physical properties. …”
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Cuproptosis-related lncRNA predicts prognosis and immune pathways in osteosarcoma patients by LIAO Jun, FENG Yanbin, XI Deshuang, ZONG Shaohui

“…The level of immune cell infiltration in tumor samples from OS patients was inferred by the ESTIMATE algorithm. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to verify the expression of the four CRLs in different cell lines. …”
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Sequence-structure based prediction of pathogenicity for amino acid substitutions in proteins associated with primary immunodeficiencies by Ekaterina S. Porfireva, Anton D. Zadorozhny, Anastasia V. Rudik, Dmitry A. Filimonov, Alexey A. Lagunin, Alexey A. Lagunin

“…SSPR models were created for each protein using the MultiPASS software based on the Bayesian algorithm and different levels of MNA (Multilevel Neighborhoods of Atoms) descriptors for the representation of structural formulas of protein fragments including AAS.ResultsThe accuracy of prediction was assessed through a 5-fold cross-validation and compared to other bioinformatics tools, such as SIFT4G, Polyphen2 HDIV, FATHMM, MetaSVM, PROVEAN, ClinPred, and Alpha Missense. …”
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Interplay of lung microbiome and epigenetics in former smokers: A machine learning and fuzzy logic approach on COPD progression by Hamidreza Taherkhani, Azadeh Kavianfar, Hossein Lanjanian, Nazanin Hosseinkhan, Mahdieh Salimi, Sajjad Nematzadeh, Mohammad Mehdi Naghizadeh, Sadegh Azimzadeh, Ali Masoudi-Nejad

“…A supervised machine learning algorithm was employed to classify preprocessed 16S rRNA data from 112 COPD patients, identify operational taxonomic units (OTUs), and assess bacterial abundance and diversity. …”
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Characteristics of gut and lung microbiota in patients with lung masses and their relationship with clinical features by Yanping Yang, Jiacheng Shen, Sulan Wei, Maosong Ye, Xing Zhao, Jian Zhou, Lin Tong, Jie Hu, Yuanlin Song, Shengdi Wu, Nuo Xu

“…The diagnostic models were constructed using microbial features identified through two approaches: random forest algorithm with five-fold cross-validation and comparative analysis of significantly differential taxa. …”
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Building radiomics models based on ACR TI-RADS combining clinical features for discriminating benign and malignant thyroid nodules by Xingxing Chen, Xingxing Chen, Lili Zhang, Bin Chen, Jiajia Lu

“…The Clin-ACR-Rad nomogram may be a more practical instrument and more accurate prediction model for malignant thyroid nodules.…”
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Effect of CCNA2 on Prognosis of Colon Cancer by Regulating Immune Microenvironment of Tumor Cells by Peng YANG, Ziyi QIU, Lingling WANG, Yuan HU, Zhengzhen CHEN, Meizhen ZHONG, Feiyue YU, Rongyuan QIU

“…Gene Set Enrichment Analysis (GSEA) was used to assess the potential molecular functions of CCNA2 in colon cancer. CIBERSORT algorithm was applied to calculate the correlation between CCNA2 and immune-cell infiltration in colon cancer. …”
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Single-cell sequencing uncovers a high ESM1-expression endothelial cell subpopulation associated with bladder cancer progression and the immunosuppressive microenvironment by Yifeng Qiu, Yuhan Wang, Jiahe Liu, Baohua Liu, Kai Sun, Qi Hou

“…Accordingly, targeting endothelial cells with high ESM1 expression may offer a novel therapeutic strategy for patients with BC.…”
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Association between age and lung cancer risk: evidence from lung lobar radiomics by Yuwei Li, Chengting Lin, Lei Cui, Chao Huang, Liting Shi, Shiyang Huang, Yue Yu, Xianglan Zhou, Qian Zhou, Kun Chen, Lei Shi

“…The minimum redundancy maximum relevance algorithm was applied to identify the top 10 age-related radiomic features among 13,137 never smokers. …”
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Serum Lipid Biomarkers for the Diagnosis and Monitoring of Neuromyelitis Optica Spectrum Disorder: Towards Improved Clinical Management by Li R, Wang J, Wang J, Xie W, Song P, Zhang J, Xu Y, Tian D, Wu L, Wang C

“…Subsequently, we validated the candidate biomarkers in the retrospective cohort and developed diagnostic models using a random forest algorithm. The association between these lipid biomarkers and disease activity was further evaluated in longitudinal analysis.Results: Our analysis identified a panel of serum lipid-related biomarkers that demonstrated significant differences between NMOSD patients and controls. …”
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Identification of Anoikis-Related Genes in Chronic Kidney Disease Based on Bioinformatics Analysis Combined with Experimental Validation by Liu H, Mei M, Zhong H, Lin S, Luo J, Huang S, Zhou J

“…Ultimately, we applied the CIBERSORTx algorithm to conduct immune infiltration analysis, classifying immune characteristics based on the abundance of 22 immune cell types.Results: To summarize, we identified 13 ARDEGs. …”
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Exploring the association between circadian rhythms and osteoporosis: new diagnostic and therapeutic targets identified via machine learning by Jian Du, Jian Du, Tian Zhou, Ran Meng, Wei Zhang, Jin Zhou, Wei Peng

“…By comparing four machine learning algorithms, the top five genes from the SVM algorithm (ECE1, FLT3, APPL1, RAB5C and FCGR2A) were determined as key genes for OP. …”
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Molecular mechanisms of efferocytosis imbalance in the idiopathic pulmonary fibrosis microenvironment: from gene screening to dynamic regulation analysis by Qian Jin, Yi Kang, Wenwen Jin, Ying Liu, Qian Chen, Jian Liu, Yali Guo, Yuguang Wang

“…Seven machine learning algorithms were employed to screen for hub genes with high diagnostic value. …”
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Differences in Characteristics of Peripartum Patients Who Did and Did Not Require an Upgrade to the Intensive Care Unit: A Propensity Score Matching Study by Jennifer A. Walker, Jerry M. Yang, Saad Pirzada, Manahel Zahid, Samantha Asuncion, Amanda Tuchler, Gillian Cooper, Allison Lankford, Emad Elsamadicy, Quincy K. Tran

“…The Classification And Regression Tree, a machine learning algorithm, was used to identify significant predictors of ICU upgrade. …”
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Comparing the performance of screening surveys versus predictive models in identifying patients in need of health-related social need services in the emergency department. by Olena Mazurenko, Adam T Hirsh, Christopher A Harle, Joanna Shen, Cassidy McNamee, Joshua R Vest

“…Machine learning (ML) predictive modeling applied to existing data sources may be a solution to systematically and effectively identify patients with HRSNs. …”
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Biological versus Clinical Risk Factors in Acute Myeloid Leukemia: Is There a Winner? by M. Malagola, N. Polverelli, V. Cancelli, E. Morello, A. Turra, E. Borlenghi, F. Cattina, B. Rambaldi, S. Bernardi, C. Zanaglio, Elif Dereli Eke, L. Gandolfi, M. Farina, D. Russo

“…., age of the patient, achievement of CR after induction, and previous history of myelodysplastic syndrome) may still impact on the treatment decision algorithm of some of these patients.…”
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The Measurement of Vital Signs in Pediatric Patients by Lifelight Software in Comparison to the Standard of Care: Protocol for the VISION-Junior Observational Study by Gauri Misra, Simon Wegerif, Louise Fairlie, Melissa Kapoor, James Fok, Gemma Salt, Jay Halbert, Ian Maconochie, Niall Mullen

“… BackgroundMeasuring vital signs (VS) is important in potentially unwell children, as a change in VS may indicate a more serious infection than is clinically apparent or herald clinical deterioration. …”
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Unraveling the role of gut microbiome in predicting adverse events in neoadjuvant therapy for rectal cancer by Jingxin Ma, Shengbo Sun, Xin Cheng, Cong Meng, Hanzheng Zhao, Wentao Fu, Yan Gao, Liyan Ma, Zhengyang Yang, Hongwei Yao, Jianrong Su

“…Some patients may develop adverse events during neoadjuvant chemoradiotherapy combined with immunotherapy, influencing response rates. …”
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Immune regulatory genes impact the hot/cold tumor microenvironment, affecting cancer treatment and patient outcomes by Mengmeng Sang, Jia Ge, Juan Ge, Juan Ge, Gu Tang, Qiwen Wang, Jiarun Wu, Liming Mao, Liming Mao, Xiaoling Ding, Xiaorong Zhou

“…The identification of these tumor immune subtypes holds clinical significance, as hot tumors may exhibit improved prognoses and heightened responsiveness to checkpoint blockade therapy. …”
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Investigating the drivers of long-term trends in the upper atmosphere over Rome across four decades by Spogli Luca, Sabbagh Dario, Perrone Loredana, Scotto Carlo, Cesaroni Claudio

“…The nature of the long-term changes in the upper atmosphere morphology at mid-latitude remains a subject of debate, particularly regarding whether these changes are purely driven by geomagnetic and solar activities or whether forcing from the lower atmosphere, such as CO2 variations, may play a role. To contribute to this debate, we investigate the nature of the long-term trends of the ionospheric and thermospheric parameters by leveraging on ionosonde data digitally recorded at the Rome Observatory since 1976. …”
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