Data Sheet 4_Identification and validation of ubiquitination-related genes for predicting cervical cancer outcome.xlsx by Ge Jin (347352)

“…Subsequently, biological markers were identified via univariate Cox regression analysis and least absolute shrinkage and selection operator algorithms. After conducting independent prognostic analysis, immune infiltration analysis was performed to investigate the immune cells that differed between the two risk subgroups. …”

Image 1_Development and validation of machine learning models for predicting STAS in stage I lung adenocarcinoma with part-solid and solid nodules: a two-center study.tif by Qing-Lin Ren (22516586)

“…Predictive features were selected using maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms. …”

Data Sheet 2_Identification and validation of ubiquitination-related genes for predicting cervical cancer outcome.csv by Ge Jin (347352)

“…Subsequently, biological markers were identified via univariate Cox regression analysis and least absolute shrinkage and selection operator algorithms. After conducting independent prognostic analysis, immune infiltration analysis was performed to investigate the immune cells that differed between the two risk subgroups. …”

Data Sheet 5_Identification and validation of ubiquitination-related genes for predicting cervical cancer outcome.xlsx by Ge Jin (347352)

“…Subsequently, biological markers were identified via univariate Cox regression analysis and least absolute shrinkage and selection operator algorithms. After conducting independent prognostic analysis, immune infiltration analysis was performed to investigate the immune cells that differed between the two risk subgroups. …”

Image 1_Correlation between metformin use and mortality in acute respiratory failure: a retrospective ICU cohort study.tif by Yunlin Yang (10277429)

“…Propensity score matching (PSM) and machine learning algorithms were used for confounder adjustment and feature selection.…”

Image 2_Correlation between metformin use and mortality in acute respiratory failure: a retrospective ICU cohort study.tif by Yunlin Yang (10277429)

“…Propensity score matching (PSM) and machine learning algorithms were used for confounder adjustment and feature selection.…”

Data Sheet 1_Identification and validation of ubiquitination-related genes for predicting cervical cancer outcome.xlsx by Ge Jin (347352)

“…Subsequently, biological markers were identified via univariate Cox regression analysis and least absolute shrinkage and selection operator algorithms. After conducting independent prognostic analysis, immune infiltration analysis was performed to investigate the immune cells that differed between the two risk subgroups. …”

Data Sheet 3_Identification and validation of ubiquitination-related genes for predicting cervical cancer outcome.csv by Ge Jin (347352)

“…Subsequently, biological markers were identified via univariate Cox regression analysis and least absolute shrinkage and selection operator algorithms. After conducting independent prognostic analysis, immune infiltration analysis was performed to investigate the immune cells that differed between the two risk subgroups. …”

Lithology mapping data of the Beishan area in China by Tao Zhang (21914624)

“…Concurrently, four L8 bands were selected through lithological spectral curve analysis to implement band ratio (BR) transformations for secondary positioning. …”

Data Sheet 1_Integration of machine learning and large language models for screening and identifying key risk factors of acute kidney injury after cardiac surgery.docx by Zishan Li (15853685)

“…Lasso regression and random forest algorithms were used to select significant predictive features from high-dimensional data. …”

<b>Supporting data for "CT Radiomics and Deep Learning Auto-segmentation in Epithelial Ovarian Carcinoma Treatment Response and Prognosis Evaluation"</b> by Mengge He (11085414)

“…</p><p dir="ltr">Second study aimed to develop a DL algorithm in segmentation of omental metastases(OM) of EOC based on staging contrast-enhanced CT (ceCT) scans of EOC patients with OM from 6 institutions and to test its utility in recurrence detection. …”

Data Sheet 4_The role and machine learning analysis of mitochondrial autophagy-related gene expression in lung adenocarcinoma.pdf by Binyu Wang (7375019)

“…To identify critical biomarkers, machine learning algorithms including Random Forest, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and Support Vector Machine (SVM) were employed. …”

Data Sheet 5_The role and machine learning analysis of mitochondrial autophagy-related gene expression in lung adenocarcinoma.zip by Binyu Wang (7375019)

“…To identify critical biomarkers, machine learning algorithms including Random Forest, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and Support Vector Machine (SVM) were employed. …”

Data Sheet 1_The role and machine learning analysis of mitochondrial autophagy-related gene expression in lung adenocarcinoma.zip by Binyu Wang (7375019)

“…To identify critical biomarkers, machine learning algorithms including Random Forest, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and Support Vector Machine (SVM) were employed. …”

Data Sheet 2_The role and machine learning analysis of mitochondrial autophagy-related gene expression in lung adenocarcinoma.pdf by Binyu Wang (7375019)

“…To identify critical biomarkers, machine learning algorithms including Random Forest, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and Support Vector Machine (SVM) were employed. …”

Data Sheet 3_The role and machine learning analysis of mitochondrial autophagy-related gene expression in lung adenocarcinoma.pdf by Binyu Wang (7375019)

“…To identify critical biomarkers, machine learning algorithms including Random Forest, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and Support Vector Machine (SVM) were employed. …”

Processed ASV data from the Insect Biome Atlas Project by Andreia Miraldo (15166741)

“…If no taxonomic assignment was above the 80% threshold, the algorithm continued to the parent rank in the taxonomy. …”

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_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. …”

DataSheet1_Screening for MicroRNA combination with engineered exosomes as a new tool against osteosarcoma in elderly patients.docx by Jiyu Han (12905564)

“…The study aimed to explore a new microRNA (miRNA) which can bind to combining engineered exosomes for treatment of older OS patients. Based on GSE65071 and miRNet 2.0, two up-regulated miRNAs (miR-328, miR-107) and seven down-regulated miRNAs (miR-133b, miR-206, miR-1-3p, miR-133a, miR-449a, miR-181daysay, miR-134) were selected. …”