Table 5_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.doc by Ting Wu (106368)

“…An artificial neural network (ANN) model was developed and evaluated. Additionally, functional enrichment, regulatory network, and drug prediction analyses were performed. …”

Table 13_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.doc by Ting Wu (106368)

“…An artificial neural network (ANN) model was developed and evaluated. Additionally, functional enrichment, regulatory network, and drug prediction analyses were performed. …”

Data Sheet 1_Development of machine learning models for predicting postoperative hyperglycemia in non-diabetic gastric cancer patients: a retrospective cohort study analysis.pdf by Nan Wang (21935)

“…The primary outcome was POH, defined as a fasting venous plasma glucose level ≥ 7.8 mmol/L within 24 hours post-surgery. Nine machine learning algorithms, including Support Vector Machine with a radial basis function kernel (SVM-radial), Random Forest, XGBoost, and Logistic Regression, were developed and compared. …”

Table 6_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…An artificial neural network (ANN) model was developed and evaluated. Additionally, functional enrichment, regulatory network, and drug prediction analyses were performed. …”

Table 11_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…An artificial neural network (ANN) model was developed and evaluated. Additionally, functional enrichment, regulatory network, and drug prediction analyses were performed. …”

Table 1_Integrative multi-omics analysis identifies a PTM-related immune signature and IRF9 as a driver in ccRCC.docx by Zixiang Li (7014416)

“…Experimental data pinpoint IRF9 as a functional driver and potential therapeutic target within this PTM-immunity axis.…”

Table 7_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…An artificial neural network (ANN) model was developed and evaluated. Additionally, functional enrichment, regulatory network, and drug prediction analyses were performed. …”

Supplementary file 1_Integrative multi-omics analysis identifies a PTM-related immune signature and IRF9 as a driver in ccRCC.docx by Zixiang Li (7014416)

“…Experimental data pinpoint IRF9 as a functional driver and potential therapeutic target within this PTM-immunity axis.…”

Table 2_Integrative multi-omics analysis identifies a PTM-related immune signature and IRF9 as a driver in ccRCC.docx by Zixiang Li (7014416)

“…Experimental data pinpoint IRF9 as a functional driver and potential therapeutic target within this PTM-immunity axis.…”

Table 10_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…An artificial neural network (ANN) model was developed and evaluated. Additionally, functional enrichment, regulatory network, and drug prediction analyses were performed. …”

Table 4_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…An artificial neural network (ANN) model was developed and evaluated. Additionally, functional enrichment, regulatory network, and drug prediction analyses were performed. …”

Spatiotemporal Soil Erosion Dataset for the Yarlung Tsangpo River Basin (1990–2100) by peng xin (21382394)

“…Bias correction was conducted using a 25-year baseline (1990–2014), with adjustments made monthly to correct for seasonal biases. The corrected bias functions were then applied to adjust the years (2020–2100) of daily rainfall data using the "ibicus" package, an open-source Python tool for bias adjustment and climate model evaluation. …”

Table 1_Identification and validation of immune and diagnostic biomarkers for interstitial cystitis/painful bladder syndrome by integrating bioinformatics and machine-learning.docx by Tao Zhou (117050)

“…The expression and biological functions of key genes in IC/BPS were further validated through in vitro experiments.…”

University of Arizona authors' scholarly works published and cited works year 2022 from OpenAlex by Yan Han (11106597)

“…</li><li><b>Data Retrieval:</b> The process involves using the oa_fetch function from the openalexR package with the entity="works" parameter and specifying the institutions.ror.…”

University of Arizona authors' scholarly works published and cited works year 2023 from OpenAlex by Yan Han (11106597)

“…</li></ul><p dir="ltr">The second dataset "works_cited", refers to <b>scholarly works cited by the publications within the </b><b>works_published</b><b> dataset</b>. …”

University of Arizona authors' scholarly works published and cited works year 2021 from OpenAlex by Yan Han (11106597)

“…</li><li><b>Data Retrieval:</b> The process involves using the oa_fetch function from the openalexR package with the entity="works" parameter and specifying the institutions.ror.…”

University of Arizona authors' scholarly works published and cited works year 2024 from OpenAlex by Yan Han (11106597)

“…</li><li><b>Data Retrieval:</b> The process involves using the oa_fetch function from the openalexR package with the entity="works" parameter and specifying the institutions.ror.…”

University of Arizona authors' scholarly works published and cited works year 2020 from OpenAlex by Yan Han (11106597)

“…</li><li><b>Data Retrieval:</b> The process involves using the oa_fetch function from the openalexR package with the entity="works" parameter and specifying the institutions.ror.…”

Examples of model selection methods for the PHMM. by Tianshu Li (1527088)

“…<i>Left panel:</i> LL on validation sets as a function of <i>m</i>. The black curves show the average log-likelihoods (across initial guesses) of all models whose algorithm converged to a solution during training (vertical bars display one standard deviation across 100 different initial guesses for the parameter values). …”

A. Explanation of the data points used by EpiFusion; B. the key parameters of the EpiFusion particle filter. by Ciara Judge (20161514)

“…Beta must vary over time and can either be fit using (i) a random walk within the particle filter, (ii) linear splines within the particle filter, (iii) MCMC fitting in epochs by fixing or fitting change times and interval values, or (iv) MCMC fitting the parameters of a logistic function which defines beta over time; C. …”