Table_2_Predicting 24-hour intraocular pressure peaks and averages with machine learning.DOCX by Ranran Chen (3308463)

“…</p>Methods<p>In this retrospective study, electronic medical records from January 2014 to May 2024 were analyzed, incorporating 24-hour IOP monitoring data and patient characteristics. Predictive models based on five machine learning algorithms were trained and evaluated. …”

Table_1_Predicting 24-hour intraocular pressure peaks and averages with machine learning.DOCX by Ranran Chen (3308463)

“…</p>Methods<p>In this retrospective study, electronic medical records from January 2014 to May 2024 were analyzed, incorporating 24-hour IOP monitoring data and patient characteristics. Predictive models based on five machine learning algorithms were trained and evaluated. …”

Data Sheet 1_Predicting clinical outcomes at hospital admission of patients with COVID-19 pneumonia using artificial intelligence: a secondary analysis of a randomized clinical tri... by Caio César Souza Conceição (21232238)

“…LASSO and CombiROC were used to select optimal predictive variables. The Youden criteria identified the best threshold for different variable combinations, which were then compared based on the highest area under the curve (AUC) and accuracy. …”

Table 1_Predicting clinical outcomes at hospital admission of patients with COVID-19 pneumonia using artificial intelligence: a secondary analysis of a randomized clinical trial.xl... by Caio César Souza Conceição (21232238)

“…LASSO and CombiROC were used to select optimal predictive variables. The Youden criteria identified the best threshold for different variable combinations, which were then compared based on the highest area under the curve (AUC) and accuracy. …”

IDWE_CHM (NRT_L) by Hao Chen (11770646)

“…</p><p dir="ltr">For a comprehensive description of the project, please refer to:<br><b>An Incremental Dynamic Weighting Ensemble Framework for Long-Term and NRT Precipitation Prediction</b><br><a href="https://figshare.com/projects/An_Incremental_Dynamic_Weighting_Ensemble_Framework_for_Long-Term_and_NRT_Precipitation_Prediction/241619" rel="noreferrer" target="_blank">https://figshare.com/projects/An_Incremental_Dynamic_Weighting_Ensemble_Framework_for_Long-Term_and_NRT_Precipitation_Prediction/241619</a></p><p><br></p><p dir="ltr">The IDWE_CHM dataset provides <b>four precipitation variables</b>, all derived from the ensemble framework but with slightly different modeling approaches:</p><ul><li><b>ENS_Reg</b> – A purely regression-based merged precipitation estimate. This product is generated by optimally weighting and combining the input datasets (ERA5-Land, IMERG, GSMaP, etc.) using regression, without additional classification. …”

Image 4_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.pdf by Liping Tang (77094)

“…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”

Image 1_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.tif by Liping Tang (77094)

“…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”

Image 7_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.tif by Liping Tang (77094)

“…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”

Data Sheet 1_Plasma methylated HIST1H3G as a non-invasive biomarker for diagnostic modeling of hepatocellular carcinoma.zip by Weiwei Zhu (251527)

“…HIST1H3G, PIVKA-II, total bilirubin (TBIL) and age were selected as the optimal markers and were included in the development of a diagnostic model. …”

Data Sheet 1_Discovery of a DNA repair-associated radiosensitivity index for predicting radiotherapy efficacy in breast cancer.docx by Jianguang Lin (13032527)

“…Accurately predicting tumor radiosensitivity is critical for optimizing therapeutic outcomes and personalizing treatment strategies. …”

Data Sheet 1_Triglyceride-glucose index and mortality in congestive heart failure with diabetes: a machine learning predictive model.doc by Lin Yu (221619)

“…The predictive performance was evaluated using seven machine learning algorithms, with the Random Survival Forest (RSF) algorithm achieving the best performance (AUC=0.817).…”

Image 2_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.pdf by Liping Tang (77094)

“…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”

IDWE_CHM (NRT_F) by Hao Chen (11770646)

“…</p><p dir="ltr">For a comprehensive description of the project, please refer to:<br><b>An Incremental Dynamic Weighting Ensemble Framework for Long-Term and NRT Precipitation Prediction</b><br><a href="https://figshare.com/projects/An_Incremental_Dynamic_Weighting_Ensemble_Framework_for_Long-Term_and_NRT_Precipitation_Prediction/241619" rel="noreferrer" target="_blank">https://figshare.com/projects/An_Incremental_Dynamic_Weighting_Ensemble_Framework_for_Long-Term_and_NRT_Precipitation_Prediction/241619</a></p><p><br></p><p dir="ltr">The IDWE_CHM dataset provides <b>four precipitation variables</b>, all derived from the ensemble framework but with slightly different modeling approaches:</p><ul><li><b>ENS_Reg</b> – A purely regression-based merged precipitation estimate. This product is generated by optimally weighting and combining the input datasets (ERA5-Land, IMERG, GSMaP, etc.) using regression, without additional classification. …”

Image 3_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.pdf by Liping Tang (77094)

“…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”

Table 1_Plasma methylated HIST1H3G as a non-invasive biomarker for diagnostic modeling of hepatocellular carcinoma.docx by Weiwei Zhu (251527)

“…HIST1H3G, PIVKA-II, total bilirubin (TBIL) and age were selected as the optimal markers and were included in the development of a diagnostic model. …”

Data Sheet 1_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.zip by Liping Tang (77094)

“…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”

Image 5_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.pdf by Liping Tang (77094)

“…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”

Data Sheet 1_Decoding the association between health level and human settlements environment: a machine learning-driven provincial analysis in China.zip by Haidong Zhu (103585)

“…The study employed the XGBoost machine learning algorithm to model the relationship between HSE and HLI. …”

Image 6_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.pdf by Liping Tang (77094)

“…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”

Data Sheet 1_Environmental sustainability indicators applied to bioprocesses: a bibliometric analysis (2005–2024).docx by Anibal Alviz-Meza (21389921)

“…The relevance of life cycle analysis as a fundamental tool is highlighted and triggered by integrating multicriteria analysis methods, optimization algorithms, and artificial intelligence. …”