Comparative analysis of clinical characteristics between ovarian cancer and ovarian cyst patients حسب Li (20568581)

"…This study aims to integrate serum biomarkers with clinical features to construct efficient diagnostic prediction models and staging prediction algorithms for ovarian cancer. This multidimensional prediction model has the potential to improve early diagnosis rates of ovarian cancer, optimize treatment decision-making processes, reduce unnecessary surgical interventions, and provide scientific basis for individualized treatment plans, ultimately improving patient prognosis and quality of life. …"

New Machine Learning Models for Predicting the Organic Cation Transporters OCT1, OCT2, and OCT3 Uptake حسب Giovanni Bocci (4275550)

"…Built using advanced decision tree ensemble algorithms and VolSurf molecular features, these models are based on the largest and most well-curated data sets available in the current literature. …"

Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites حسب Gustav Olanders (3711889)

"…Recently, a breakthrough in computational biology has emerged with the development of deep learning algorithms capable of predicting protein structures based on their amino acid sequences (Jumper, J., et al. …"

All the Places I Have Lived v3.0 حسب Christian Berg (18034411)

"…<br><br>To take this further, I developed a small Python-based tool I call Memory Infuser. …"

Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites حسب Gustav Olanders (3711889)

"…Recently, a breakthrough in computational biology has emerged with the development of deep learning algorithms capable of predicting protein structures based on their amino acid sequences (Jumper, J., et al. …"

Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites حسب Gustav Olanders (3711889)

"…Recently, a breakthrough in computational biology has emerged with the development of deep learning algorithms capable of predicting protein structures based on their amino acid sequences (Jumper, J., et al. …"

Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites حسب Gustav Olanders (3711889)

"…Recently, a breakthrough in computational biology has emerged with the development of deep learning algorithms capable of predicting protein structures based on their amino acid sequences (Jumper, J., et al. …"

Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites حسب Gustav Olanders (3711889)

"…Recently, a breakthrough in computational biology has emerged with the development of deep learning algorithms capable of predicting protein structures based on their amino acid sequences (Jumper, J., et al. …"

Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites حسب Gustav Olanders (3711889)

"…Recently, a breakthrough in computational biology has emerged with the development of deep learning algorithms capable of predicting protein structures based on their amino acid sequences (Jumper, J., et al. …"

Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites حسب Gustav Olanders (3711889)

"…Recently, a breakthrough in computational biology has emerged with the development of deep learning algorithms capable of predicting protein structures based on their amino acid sequences (Jumper, J., et al. …"

The ROC curve for the experiment. حسب Mahade Hasan (20536430)

"…In response to this need, we applied nine machine learning algorithms XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector machine (SVM), gaussian naïve bayes (NB gaussian), adaptive boosting, and linear regression to predict heart disease based on a range of physiological indicators. …"

System architecture of this study. حسب Mahade Hasan (20536430)

"…In response to this need, we applied nine machine learning algorithms XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector machine (SVM), gaussian naïve bayes (NB gaussian), adaptive boosting, and linear regression to predict heart disease based on a range of physiological indicators. …"

Description of the train test split dataset. حسب Mahade Hasan (20536430)

"…In response to this need, we applied nine machine learning algorithms XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector machine (SVM), gaussian naïve bayes (NB gaussian), adaptive boosting, and linear regression to predict heart disease based on a range of physiological indicators. …"

The dataset’s summarized description. حسب Mahade Hasan (20536430)

"…In response to this need, we applied nine machine learning algorithms XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector machine (SVM), gaussian naïve bayes (NB gaussian), adaptive boosting, and linear regression to predict heart disease based on a range of physiological indicators. …"

Feature selection procedure. حسب Mahade Hasan (20536430)

"…In response to this need, we applied nine machine learning algorithms XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector machine (SVM), gaussian naïve bayes (NB gaussian), adaptive boosting, and linear regression to predict heart disease based on a range of physiological indicators. …"

Histogram of attributes. حسب Mahade Hasan (20536430)

"…In response to this need, we applied nine machine learning algorithms XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector machine (SVM), gaussian naïve bayes (NB gaussian), adaptive boosting, and linear regression to predict heart disease based on a range of physiological indicators. …"

Illustration of all features correlation. حسب Mahade Hasan (20536430)

"…In response to this need, we applied nine machine learning algorithms XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector machine (SVM), gaussian naïve bayes (NB gaussian), adaptive boosting, and linear regression to predict heart disease based on a range of physiological indicators. …"

Data Sheet 1_Predicting axillary lymph node metastasis in breast cancer using a multimodal radiomics and deep learning model.docx حسب Fuyu Guo (4588312)

"…Objective<p>To explore the value of combined radiomics and deep learning models using different machine learning algorithms based on mammography (MG) and magnetic resonance imaging (MRI) for predicting axillary lymph node metastasis (ALNM) in breast cancer (BC). …"

Data Sheet 1_Accurate informatic modeling of tooth enamel pellicle interactions by training substitution matrices with Mat4Pep.doc حسب Jeremy Horst Keeper (20458274)

"…We show that tooth enamel pellicle peptides contain subtle sequence similarities that encode hydroxyapatite binding mechanisms by segregating pellicle peptides from control sequences using our previously developed substitution matrix-based peptide comparison protocol with improvements. …"

Data Sheet 1_Following intravenous thrombolysis, the outcome of diabetes mellitus associated with acute ischemic stroke was predicted via machine learning.docx حسب Xiaoqing Liu (196900)

"…The findings underscore the effectiveness of machine learning algorithms, particularly XGB, in predicting functional outcomes in diabetic AIS patients, providing clinicians with a valuable tool for treatment planning and improving patient outcome predictions based on receiver operating characteristic (ROC) analysis and accuracy assessments.…"