Table 10_Transcriptomic insights into the mechanism of action of telomere-related biomarkers in rheumatoid arthritis.xlsx by Lijuan Feng (3746086)

“…Biomarkers were subsequently identified using machine learning algorithms, receiver operating characteristic analysis, and expression level comparisons between RA and control samples. …”

Table 2_Transcriptomic insights into the mechanism of action of telomere-related biomarkers in rheumatoid arthritis.xlsx by Lijuan Feng (3746086)

“…Biomarkers were subsequently identified using machine learning algorithms, receiver operating characteristic analysis, and expression level comparisons between RA and control samples. …”

Table 5_Transcriptomic insights into the mechanism of action of telomere-related biomarkers in rheumatoid arthritis.xlsx by Lijuan Feng (3746086)

“…Biomarkers were subsequently identified using machine learning algorithms, receiver operating characteristic analysis, and expression level comparisons between RA and control samples. …”

Table 8_Transcriptomic insights into the mechanism of action of telomere-related biomarkers in rheumatoid arthritis.xlsx by Lijuan Feng (3746086)

“…Biomarkers were subsequently identified using machine learning algorithms, receiver operating characteristic analysis, and expression level comparisons between RA and control samples. …”

Table 4_Transcriptomic insights into the mechanism of action of telomere-related biomarkers in rheumatoid arthritis.xlsx by Lijuan Feng (3746086)

“…Biomarkers were subsequently identified using machine learning algorithms, receiver operating characteristic analysis, and expression level comparisons between RA and control samples. …”

Table 1_Transcriptomic insights into the mechanism of action of telomere-related biomarkers in rheumatoid arthritis.xlsx by Lijuan Feng (3746086)

“…Biomarkers were subsequently identified using machine learning algorithms, receiver operating characteristic analysis, and expression level comparisons between RA and control samples. …”

Table 1_Plasma exosomal lncRNA-related signatures define molecular subtypes and predict survival and treatment response in hepatocellular carcinoma.docx by Fangmin Zhong (17415318)

“…</p>Methods<p>The transcriptomic data from 230 plasma exosomes and 831 HCC tissues were integrated. …”

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

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

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. …”

<b>Neural Symbolic Vault: Symbolic Species and</b> <b>DNA Co-Encoding Research Bundle v1.0 (A+M[S] Archive)</b> by Jeffrey Siergiej (20937434)

“…Symbolic DNA encoding structures, vector libraries, and derived algorithms are under private protection via in-house symbolic token ledger and mutation tracking systems.…”

Data Sheet 1_Evaluating the effectiveness of AI-enhanced “One Body, Two Wings” pharmacovigilance models in China: a nationwide survey on medication safety and risk management.pdf... by Jian Yang (12136)

“…As the pharmaceutical landscape grows more complex, integrating AI into pharmacovigilance offers the potential to enhance adverse drug reaction (ADR) detection and monitoring.</p>Methods<p>A nationwide cross-sectional survey was conducted from June 25 to August 10, 2024, involving 1,000 participants from pharmacovigilance centers, hospitals, corporations, and the general public. …”

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). …”

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. …”

Deep Diving into deep learning and AI: Experiences and lessons learned by eRNZ Admin (6438486)

“…To overcome this, we are moving to a new stage where we are maturing the tools to make them more accessible to the wider research community, as well as developing online training material to enable uptake by non-data scientists. …”