The overall framework of this study. by Tianbao Feng (21722233)

“…Additionally, a protein-protein interaction (PPI) network was established to identify hub genes, and 8 machine learning algorithms were used to narrowed down hub genes. <i>BMX</i> and <i>CASP5</i> were consistently identified across all algorithms. …”

PANoptosis related genes. by Tianbao Feng (21722233)

“…Additionally, a protein-protein interaction (PPI) network was established to identify hub genes, and 8 machine learning algorithms were used to narrowed down hub genes. <i>BMX</i> and <i>CASP5</i> were consistently identified across all algorithms. …”

Integrating cellular experiments, single-cell sequencing, and machine learning to identify endoplasmic reticulum stress biomarkers in idiopathic pulmonary fibrosis by Yi Liao (575994)

“…Diagnostic and prognostic models were developed using machine learning algorithms and validated across both training and validation sets. …”

Image 1_Deciphering the role of metal ion transport-related genes in T2D pathogenesis and immune cell infiltration via scRNA-seq and machine learning.tif by Zuhui Pu (10931751)

“…We employed 12 machine learning algorithms to develop predictive models and assessed immune cell infiltration using single-sample gene set enrichment analysis (ssGSEA). …”

Equitable Hospital Length of Stay Prediction for Patients with Learning Disabilities and Multiple Long-term Conditions Using Machine Learning by Emeka Abakasanga (14235503)

“…</p><p dir="ltr"><b>Conclusion:</b> This study demonstrates the feasibility of applying ML models to predict LOS for patients with LD and MLTCs, while addressing fairness through bias mitigation techniques. The findings highlight the potential for equitable healthcare predictions using EHR data, paving the way for improved clinical decision-making and resource management.…”

Image 2_Deciphering the role of metal ion transport-related genes in T2D pathogenesis and immune cell infiltration via scRNA-seq and machine learning.tif by Zuhui Pu (10931751)

“…We employed 12 machine learning algorithms to develop predictive models and assessed immune cell infiltration using single-sample gene set enrichment analysis (ssGSEA). …”

Abbreviations used in the text. by Lorenzo Ruinelli (13014138)

“…Machine Learning (ML) algorithms were developed with 10-fold cross-validation, and diagnostic accuracy was evaluated.…”

<b>PRISMA-P checklist for</b><b> “Protocol for Conducting a Scoping Review on The Use of AI in Automated Scoring of Short-Answer Questions in Medical Education”</b> by S. Ayhan ÇALIŞKAN (22626020)

“…Despite promising developments, concerns persist regarding algorithmic transparency, data privacy, and the reliability and validity of automated scoring compared with human graders. …”

Table 1_Exploring fecal microbiota signatures associated with immune response and antibiotic impact in NSCLC: insights from metagenomic and machine learning approaches.docx by Wenjie Han (1812673)

“…Among eight machine learning algorithms evaluated, the optimal model was selected to construct a predictive framework for immunotherapy response.…”

EKC virus-specific HMGB1 secretion. by Amrita Saha (1771837)

“…Sequence differences across types are color coded and sequence conservation are shown with dots. …”

Primer sequences of <i>Bm</i>x and β-actin. by Tianbao Feng (21722233)

“…Additionally, a protein-protein interaction (PPI) network was established to identify hub genes, and 8 machine learning algorithms were used to narrowed down hub genes. <i>BMX</i> and <i>CASP5</i> were consistently identified across all algorithms. …”

Generative AI and Journalism: Content, Journalistic Perceptions, and Audience Experiences by T.J. Thomson (9015119)

“…These biases exist because of human biases embedded in training data and/or the conscious or unconscious biases of those who develop AI algorithms and models. …”

Table 1_Demethylase FTO mediates m6A modification of ENST00000619282 to promote apoptosis escape in rheumatoid arthritis and the intervention effect of Xinfeng Capsule.docx by Fanfan Wang (1669474)

“…The m6A modification of long non-coding RNAs (lncRNAs) plays a critical regulatory role in RA pathogenesis. …”

Image 1_Construction of a diagnostic model and identification of effect genes for diabetic kidney disease with concurrent vascular calcification based on bioinformatics and multipl... by Lili Huang (125493)

“…</p>Methods<p>RNA sequencing (Bulk-seq) data of DKD and VC from various species were obtained from the Gene Expression Omnibus (GEO) database, and relevant datasets were integrated. …”

Post-marketing safety associated with sodium zirconium cyclosilicate: a pharmacovigilance study based on the FDA reporting system by Jiankang Chen (20950641)

“…Accordingly, the objective of this study was to investigate real-world adverse events (AEs) associated with SZC using the FDA Adverse Event Reporting System (FAERS).</p> <p>Relevant data regarding SZC were extracted from FAERS, and signal detection was conducted using four distinct algorithms. …”

Supplementary information files for "Equitable hospital length of stay prediction for patients with learning disabilities and multiple long-term conditions using machine learning" by Emeka Abakasanga (14235503)

“…</p><p dir="ltr"><b>Conclusion:</b> This study demonstrates the feasibility of applying ML models to predict LOS for patients with LD and MLTCs, while addressing fairness through bias mitigation techniques. The findings highlight the potential for equitable healthcare predictions using EHR data, paving the way for improved clinical decision-making and resource management<br><br>©The Author(s), CC BY-4.0</p>…”

Housekeeping and unexpressed genes. by Zhiwei Guo (335819)

“…Using four machine learning models and two feature selection algorithms, we developed classifiers for predicting preterm birth. …”

Table 11_Utilizing bioinformatics to identify biomarkers and analyze their expression in relation to immune cell ratios in femoral head necrosis.xlsx by Dongchen Li (11568169)

“…In addition, we performed expression data visualization and ROC curve analysis on the external dataset GSE74089 to further evaluate the discriminative power of the hub genes. …”

Image 4_Machine learning-based diagnostic and prognostic models for breast cancer: a new frontier on the clinical application of natural killer cell-related gene signatures in prec... by Yutong Fang (16621143)

“…</p>Methods<p>We collected transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. …”

Table 5_Utilizing bioinformatics to identify biomarkers and analyze their expression in relation to immune cell ratios in femoral head necrosis.xlsx by Dongchen Li (11568169)

“…In addition, we performed expression data visualization and ROC curve analysis on the external dataset GSE74089 to further evaluate the discriminative power of the hub genes. …”