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. …”

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. …”

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. …”

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. …”

Table_1_Reinforcement learning for watershed and aquifer management: a nationwide view in the country of Mexico with emphasis in Baja California Sur.XLSX by Roberto Ortega (18525597)

“…<p>Reinforcement Learning (RL) is a method that teaches agents to make informed decisions in diverse environments through trial and error, aiming to maximize a reward function and discover the optimal Q-learning function for decision-making. …”

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

“…Background<p>The aim of this study was to investigate the association of metformin use with the risk of in-hospital mortality and prognosis in acute respiratory failure (ARF) patients admitted to the intensive care unit (ICU).…”

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

“…Background<p>The aim of this study was to investigate the association of metformin use with the risk of in-hospital mortality and prognosis in acute respiratory failure (ARF) patients admitted to the intensive care unit (ICU).…”

Table1_Warfarin anticoagulation management during the COVID-19 pandemic: The role of internet clinic and machine learning.DOCX by Meng-Fei Dai (12324617)

“…However, social distancing and stay-at-home orders, which were universally adopted strategies to avoid the spread of COVID-19, led to unprecedented challenges. This study aimed to optimize warfarin treatment during the COVID-19 pandemic by determining the role of the Internet clinic and developing a machine learning (ML) model to predict anticoagulation quality.…”

Table 1_The future of critical care: AI-powered mortality prediction for acute variceal gastrointestinal bleeding and acute non-variceal gastrointestinal bleeding patients.docx by Zhou Liu (1506679)

“…In case of ANGIB patients, gradient boosting model proven to be the optimal machine learning models, with the AUC of 0.985 ± 0.002, accuracy of 0.948 ± 0.009, precision of 0.949 ± 0.009, recall of 0.968 ± 0.009, and F1 score of 0.959 ± 0.007. …”

Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles by Soham Savarkar (21811825)

“…</p><p dir="ltr">Encoding: Categorical variables such as surface coating and cell type were grouped into logical classes and label-encoded to enable model compatibility.</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…”