CANDID-II Dataset by Sijing Feng (10187216)

“…This dataset can be used for training and testing for deep learning algorithms for adult chest x rays.</p><p dir="ltr">Unfortunately, since Feb 2024, the New Zealand government is changing the data governance on datasets used for AI development and this affects the process of how the CANDID II dataset is to be accessed by the external users. …”

Table 2_Artificial intelligence in vaccine research and development: an umbrella review.docx by Rabie Adel El Arab (14113425)

“…Nonetheless, persistent challenges emerged—data heterogeneity, algorithmic bias, limited regulatory frameworks, and ethical concerns over transparency and equity.…”

Table 3_Artificial intelligence in vaccine research and development: an umbrella review.docx by Rabie Adel El Arab (14113425)

“…Nonetheless, persistent challenges emerged—data heterogeneity, algorithmic bias, limited regulatory frameworks, and ethical concerns over transparency and equity.…”

Table 1_Artificial intelligence in vaccine research and development: an umbrella review.docx by Rabie Adel El Arab (14113425)

“…Nonetheless, persistent challenges emerged—data heterogeneity, algorithmic bias, limited regulatory frameworks, and ethical concerns over transparency and equity.…”

Data Sheet 1_Multi-omics derivation of a core gene signature for predicting therapeutic response and characterizing immune dysregulation in inflammatory bowel disease.csv by Mingming Wang (394106)

“…Current precision medicine approaches lack robust molecular tools integrating transcriptomic signatures with immune dynamics for personalized treatment guidance.</p>Methods<p>We performed multi-omics analyses of GEO datasets using machine learning algorithms (LASSO/Random Forest) to derive a four-gene signature. …”

Image 1_Multi-omics derivation of a core gene signature for predicting therapeutic response and characterizing immune dysregulation in inflammatory bowel disease.jpeg by Mingming Wang (394106)

“…Current precision medicine approaches lack robust molecular tools integrating transcriptomic signatures with immune dynamics for personalized treatment guidance.</p>Methods<p>We performed multi-omics analyses of GEO datasets using machine learning algorithms (LASSO/Random Forest) to derive a four-gene signature. …”

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. …”

CANDID-III Dataset by Sijing Feng (10187216)

“…This dataset can be used for training and testing for deep learning algorithms for adult chest x rays.</p><p dir="ltr">Unfortunately, since Feb 2024, the New Zealand government is changing the data governance on datasets used for AI development and this affects the process of how the CANDID III dataset is to be accessed by the external users. …”

Image 13_Immune intrinsic escape signature stratifies prognosis, characterizes the tumor immune microenvironment, and identifies tumorigenic PPP1R8 in glioblastoma multiforme patie... by Ran Du (552530)

“…</p>Conclusion<p>The IERGs-based signature offers reliable prognostication for GBM, validated across multiple datasets. …”

Image 1_Polyamine metabolism related gene index prediction of prognosis and immunotherapy response in breast cancer.jpeg by Ruoya Wang (842048)

“…This study aimed to determine whether polyamine metabolism-related genes (PMRGs) could predict prognosis and immunotherapy efficacy in Breast Cancer (BC).</p>Methods<p>We conducted a comprehensive multi-omics analysis of PMRG expression profiles in BC. …”

Table 3_Unveiling ammonia-induced cell death: a new frontier in clear cell renal cell carcinoma prognosis.xlsx by Peize Yu (21837977)

“…Differentially expressed AICD-related genes were identified through differential expression analysis, univariate Cox regression, and machine learning algorithms (LASSO, random forest, and CoxBoost). A prognostic risk model was developed via multivariate Cox regression. …”

Image 4_Immune intrinsic escape signature stratifies prognosis, characterizes the tumor immune microenvironment, and identifies tumorigenic PPP1R8 in glioblastoma multiforme patien... by Ran Du (552530)

“…</p>Conclusion<p>The IERGs-based signature offers reliable prognostication for GBM, validated across multiple datasets. …”

Data Sheet 1_Neutrophil-to-lymphocyte ratio predicts inpatient gout recurrence: a large-scale multicenter retrospective cohort with machine-learning validation.pdf by Hui Zhang (7197)

“…This study aims to investigate the association of NLR with inpatient gout recurrence, and compare its performance with traditional markers.</p>Methods<p>In this international, multicenter retrospective cohort study, hospitalized patients with gout were enrolled from the GoutRe cohort (China, 2010-2025) and MIMIC-IV cohort (USA, 2008-2019). …”

Image 3_Unveiling ammonia-induced cell death: a new frontier in clear cell renal cell carcinoma prognosis.tif by Peize Yu (21837977)

“…Differentially expressed AICD-related genes were identified through differential expression analysis, univariate Cox regression, and machine learning algorithms (LASSO, random forest, and CoxBoost). A prognostic risk model was developed via multivariate Cox regression. …”

DataSheet1_Extrachromosomal circular DNAs in prostate adenocarcinoma: global characterizations and a novel prediction model.PDF by Qingliu He (12528332)

“…The immune microenvironment of the risk model was quantified using a variety of immunological algorithms, which also identified its characteristics with regard to immunotherapy, immune response, and immune infiltration.…”

Supplementary file 2_The role of α-hydroxybutyrate in modulating sepsis progression: identification of key targets and biomarkers through multi-database data mining, machine learni... by Qing Lu (28914)

“…Functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms (L1-LASSO, RF, and SVM) were applied to identify biomarkers. …”

Image 5_Immune intrinsic escape signature stratifies prognosis, characterizes the tumor immune microenvironment, and identifies tumorigenic PPP1R8 in glioblastoma multiforme patien... by Ran Du (552530)

“…</p>Conclusion<p>The IERGs-based signature offers reliable prognostication for GBM, validated across multiple datasets. …”

Image 10_Immune intrinsic escape signature stratifies prognosis, characterizes the tumor immune microenvironment, and identifies tumorigenic PPP1R8 in glioblastoma multiforme patie... by Ran Du (552530)

“…</p>Conclusion<p>The IERGs-based signature offers reliable prognostication for GBM, validated across multiple datasets. …”

Image 8_Immune intrinsic escape signature stratifies prognosis, characterizes the tumor immune microenvironment, and identifies tumorigenic PPP1R8 in glioblastoma multiforme patien... by Ran Du (552530)

“…</p>Conclusion<p>The IERGs-based signature offers reliable prognostication for GBM, validated across multiple datasets. …”

Table 4_Immune intrinsic escape signature stratifies prognosis, characterizes the tumor immune microenvironment, and identifies tumorigenic PPP1R8 in glioblastoma multiforme patien... by Ran Du (552530)

“…</p>Conclusion<p>The IERGs-based signature offers reliable prognostication for GBM, validated across multiple datasets. …”