Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites by 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 by 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 by 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 by 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 by 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 by 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. by 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. by 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. by 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. by 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. by 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. by 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. by 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 by 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 by 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 by 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.…”

Image 1_Multi-omics integration analysis based on plasma circulating proteins reveals potential therapeutic targets for ulcerative colitis.pdf by Jihai Zhou (1876561)

“…This study aims to identify potential diagnostic and therapeutic biomarkers for UC through multi-omics integrative analysis, providing new insights into its precise diagnosis and treatment.</p>Methods<p>Data samples from the Gene Expression Omnibus database and protein quantitative trait loci data from genome-wide association studies were integrated to identify overlapping genes. …”

Estilometría TIP: enhanced text analysis tool with customisable metrics for Spanish texts by Francisco J. Carreras-Riudavets (20562834)

“…However, most tools are developed for English, limiting their effectiveness for Spanish texts, which involve complex inflections. …”

Table 1_Ensemble machine learning for predicting renal function decline in chronic kidney disease: development and external validation.docx by Hong Chen (108084)

“…Clinical, demographic, and laboratory data were processed with rigorous quality control. …”

Table 1_Ensemble machine learning models for predicting bone metastasis in bladder cancer.docx by Zhan Jiang Yu (22081106)

“…Currently, the accurate prediction of BM in BC remains a challenge. This study develops predictive models using machine learning algorithms to predict bladder cancer bone metastasis (BCBM) and aid in personalized clinical decisions.…”