Image 1_Identification of biomarkers and immune microenvironment associated with pterygium through bioinformatics and machine learning.pdf by Li-Wei Zhang (2184769)

“…Enrichment analyses were performed to assess the biological processes and signaling pathways associated with these DEGs. …”

Image 3_Identification of biomarkers and immune microenvironment associated with pterygium through bioinformatics and machine learning.pdf by Li-Wei Zhang (2184769)

“…Enrichment analyses were performed to assess the biological processes and signaling pathways associated with these DEGs. …”

Table 4_Identification of biomarkers and immune microenvironment associated with pterygium through bioinformatics and machine learning.xlsx by Li-Wei Zhang (2184769)

“…Enrichment analyses were performed to assess the biological processes and signaling pathways associated with these DEGs. …”

Table 1_Identification of biomarkers and immune microenvironment associated with pterygium through bioinformatics and machine learning.xlsx by Li-Wei Zhang (2184769)

“…Enrichment analyses were performed to assess the biological processes and signaling pathways associated with these DEGs. …”

Table 2_Identification of biomarkers and immune microenvironment associated with pterygium through bioinformatics and machine learning.xlsx by Li-Wei Zhang (2184769)

“…Enrichment analyses were performed to assess the biological processes and signaling pathways associated with these DEGs. …”

Benchmark data and code for evaluating and enhancing spatial cognition abilities of large language models (LLMs) by Yang (16534473)

“…Additionally, we propose Hybrid Mind, a tool-augmented approach that integrates LLMs with deterministic GIS algorithms to enhance their performance in spatial cognitive tasks. …”

Table 1_Correlation of triglyceride-glucose index with the incidence and prognosis of hyperglycemic crises in critically ill patients with diabetes mellitus: a machine-learning-bas... by Mingchen Xie (4325692)

“…This study aims to evaluate the relationship between the TyG index and HCE incidence/clinical outcomes in critically ill patients with DM and to construct a risk prediction model using machine-learning algorithms.</p>Methods<p>This multi-center retrospective investigation leveraged clinical repositories from Medical Information Mart for Intensive Care IV (MIMIC-IV) and eICU Collaborative Research Database (eICU-CRD). …”

Table1_Examining epigenetic aging in the post-mortem brain in attention deficit hyperactivity disorder.DOCX by Gauri G. Shastri (7909907)

“…We consider reasons for this unexpected finding, including the limited sampling of brain regions, the age range of individuals studied, and the possibility that processes that accelerate epigenetic age may be counteracted by the developmental delay posited in some models of ADHD.…”

Supporting data for "Fracture and Non-linear Response of Biopolymer Network with Dynamic Cross-linkers" by Bingxian Tang (9151802)

“…It details the input parameters, such as line density and crosslink number, as well as the algorithms employed for filament arrangement and crosslink determination. …”

Table 1_The future of critical care: AI-powered mortality prediction for acute variceal gastrointestinal bleeding and acute non-variceal gastrointestinal bleeding patients.docx by Zhou Liu (1506679)

“…The Synthetic Minority Over-sampling Technique-Edited Nearest Neighbors (SMOTE-ENN) and Adaptive Synthetic Sampling (ADASYN) were adopted to address the imbalance of the dataset. …”