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.…"

Estilometría TIP: enhanced text analysis tool with customisable metrics for Spanish texts حسب 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 models for predicting bone metastasis in bladder cancer.docx حسب 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.…"

Visualized hysteroscopic artificial intelligence fertility assessment system for endometrial injury: an image-deep-learning study حسب Bohan Li (4869760)

"…<p>Asherman’s syndrome (AS) is a significant cause of subfertility in women from developing countries. Over 80% of AS cases in these regions are linked to dilation and curettage (D&C) procedures following pregnancy. …"

Data Sheet 1_Advances and prospects in reconstruction approaches for snow cover mapping using polar-orbiting satellites.docx حسب Jun Zhang (48506)

"…This paper categorizes current reconstruction approaches into eight types: temporal filters, spatial filters, multisensor fusion, and the hidden Markov random field (HMRF) model for BSC mapping, as well as temporal and spatial interpolation methods, spatiotemporal reconstruction algorithms, machine learning-based reconstruction techniques, and data assimilation methods for NDSI or FSC mapping. …"

Image 3_High-parameter immunophenotyping reveals distinct immune cell profiles in pruritic dogs and cats.png حسب Erin McDonald (3535409)

"…These animals were categorized based on pruritic behavior or routine check-ups (non-pruritic controls).…"

Image 4_High-parameter immunophenotyping reveals distinct immune cell profiles in pruritic dogs and cats.png حسب Erin McDonald (3535409)

"…These animals were categorized based on pruritic behavior or routine check-ups (non-pruritic controls).…"