Simulation metrics for the number of counts. by Thomas A. Trikalinos (5785877)

“…The package’s functions are based on three algorithms that provably sample from a target NHPPP: the time-transformation of a homogeneous Poisson process (of intensity one) via the inverse of the integrated intensity function; the generation of a Poisson number of order statistics from a fixed density function; and the thinning of a majorizing NHPPP via an acceptance-rejection scheme. …”

Table 2_Integration of single-cell sequencing and machine learning identifies key macrophage-associated genetic signatures in lumbar disc degeneration.xlsx by Hongxing Zhang (209372)

“…A combined diagnostic model for LDD risk was developed based on the expression profiles of the diagnostic genes. …”

Table 1_Bioinformatic analysis identifies LPL as a critical gene in diabetic kidney disease via lipoprotein metabolism.xlsx by Qian Dong (414788)

“…These findings lay a theoretical foundation for understanding the molecular mechanisms of DKD and developing LPL-based therapeutic strategies.</p>…”

Table 1_Integration of single-cell sequencing and machine learning identifies key macrophage-associated genetic signatures in lumbar disc degeneration.xlsx by Hongxing Zhang (209372)

“…A combined diagnostic model for LDD risk was developed based on the expression profiles of the diagnostic genes. …”

Table 4_Integration of single-cell sequencing and machine learning identifies key macrophage-associated genetic signatures in lumbar disc degeneration.xlsx by Hongxing Zhang (209372)

“…A combined diagnostic model for LDD risk was developed based on the expression profiles of the diagnostic genes. …”

Data Sheet 1_Identification of SMYD2 as a candidate diagnostic and prognostic biomarker for gastric cancer.docx by Sichao Wang (2098546)

“…Background<p>Histone modification enzymes (HMEs) are associated with cancer development, treatment response, and prognosis. However, the potential roles of HMEs in gastric cancer (GC) remain unclear. …”

Table 3_Integration of single-cell sequencing and machine learning identifies key macrophage-associated genetic signatures in lumbar disc degeneration.xlsx by Hongxing Zhang (209372)

“…A combined diagnostic model for LDD risk was developed based on the expression profiles of the diagnostic genes. …”

Data Sheet 2_Integration of single-cell sequencing and machine learning identifies key macrophage-associated genetic signatures in lumbar disc degeneration.pdf by Hongxing Zhang (209372)

“…A combined diagnostic model for LDD risk was developed based on the expression profiles of the diagnostic genes. …”

Data Sheet 1_Integration of single-cell sequencing and machine learning identifies key macrophage-associated genetic signatures in lumbar disc degeneration.pdf by Hongxing Zhang (209372)

“…A combined diagnostic model for LDD risk was developed based on the expression profiles of the diagnostic genes. …”

Table 1_Identification of crosstalk genes and diagnostic biomarkers in systemic sclerosis associated sarcopenia through integrative analysis and machine learning.docx by Yanfang Wu (206076)

“…Crosstalk genes (CGs) were identified using least absolute shrinkage and selection operator (LASSO) regularization, ensemble decision trees, and support vector machine-based feature selection. Machine learning algorithms were employed to construct a predictive scoring model and to assess the diagnostic value of key biomarkers. …”

Data Sheet 1_Bioinformatic analysis identifies LPL as a critical gene in diabetic kidney disease via lipoprotein metabolism.pdf by Qian Dong (414788)

“…These findings lay a theoretical foundation for understanding the molecular mechanisms of DKD and developing LPL-based therapeutic strategies.</p>…”

Data Sheet 1_Risk factor screening and predictive modeling of time-in-range in patients with T2DM undergoing SIIT therapy.docx by Yujia Liu (318262)

“…Prediction models were constructed separately using logistic regression and random forest algorithms. Additionally, a nomogram was developed for facilitating clinical application.…”

Data Sheet 1_Tumor tissue-of-origin classification using miRNA-mRNA-lncRNA interaction networks and machine learning methods.docx by Ankita Lawarde (16544943)

“…This work highlights the power of combining miRNA network biology with ML to improve precision oncology diagnostics and supports future development of liquid biopsy-based cancer classification.…”

Image 1_Integrated multi-omics analysis and machine learning identify G protein-coupled receptor-related signatures for diagnosis and clinical benefits in soft tissue sarcoma.jpeg by Duo Wang (1787634)

“…We developed a novel machine learning framework that incorporated 12 machine learning algorithms and their 127 combinations to construct a consensus GPRS to screen biomarkers with diagnostic significance and clinical translation, which was assessed by the internal and external validation datasets. …”

Table 1_Integrated multi-omics analysis and machine learning identify G protein-coupled receptor-related signatures for diagnosis and clinical benefits in soft tissue sarcoma.xlsx by Duo Wang (1787634)

“…We developed a novel machine learning framework that incorporated 12 machine learning algorithms and their 127 combinations to construct a consensus GPRS to screen biomarkers with diagnostic significance and clinical translation, which was assessed by the internal and external validation datasets. …”

Harnessing Deep Learning and Microblogs for Real-Time Resource Allocation in Emergency Scenarios by Eman Ansar (22127696)

“…</p><p dir="ltr">While the implementation of actual resource allocation algorithms remains future work, our research establishes a robust foundation for developing predictive models that can anticipate resource needs during emergencies, potentially transforming how disaster response is coordinated.…”

<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.…”

Particle swarm optimization method for energy management of the hybrid system of an electric vehicle charging station by Olukorede Adenuga (20139099)

“…The developed PSO methods and algorithms can be useful for the resolution of numerous energy management problems in smart grid applications, provincial and national control centers, and research and educational institutions.…”

DRF-main.zip by Pranuthi Gogumalla (21392648)

“…In this research, we used Sentinel-1 and Sentinel-2 datasets, by processing them on the Google Earth Engine platform and developed several crop-based indicators to assess crop phenology as well as the distinction between a well-managed field (demo plots) vs a normal farmers' practice-managed crop (control plots) using Sentinel-1 satellite data. …”