DataSheet5_LncRNA model predicts liver cancer drug resistance and validate in vitro experiments.CSV by Qiushi Yin (1661437)

“…Machine learning algorithms including random forest (RF), lasso regression (LR), and support vector machines (SVMs) were used to identify important chemotherapy-related LncRNAs. …”

Image3_Identification and Validation of the Diagnostic Characteristic Genes of Ovarian Cancer by Bioinformatics and Machine Learning.TIF by Jinya Liu (12705179)

“…Next, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO) enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were performed for functional enrichment analysis of these DEGs. Then, two machine learning algorithms, Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were used to get the diagnostic genes. …”

DataSheet3_LncRNA model predicts liver cancer drug resistance and validate in vitro experiments.CSV by Qiushi Yin (1661437)

“…Machine learning algorithms including random forest (RF), lasso regression (LR), and support vector machines (SVMs) were used to identify important chemotherapy-related LncRNAs. …”

DataSheet2_LncRNA model predicts liver cancer drug resistance and validate in vitro experiments.CSV by Qiushi Yin (1661437)

“…Machine learning algorithms including random forest (RF), lasso regression (LR), and support vector machines (SVMs) were used to identify important chemotherapy-related LncRNAs. …”

Image1_Identification and Validation of the Diagnostic Characteristic Genes of Ovarian Cancer by Bioinformatics and Machine Learning.TIF by Jinya Liu (12705179)

“…Next, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO) enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were performed for functional enrichment analysis of these DEGs. Then, two machine learning algorithms, Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were used to get the diagnostic genes. …”

Image6_Identification and Validation of the Diagnostic Characteristic Genes of Ovarian Cancer by Bioinformatics and Machine Learning.TIF by Jinya Liu (12705179)

“…Next, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO) enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were performed for functional enrichment analysis of these DEGs. Then, two machine learning algorithms, Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were used to get the diagnostic genes. …”

DataSheet1_Identification and Validation of the Diagnostic Characteristic Genes of Ovarian Cancer by Bioinformatics and Machine Learning.ZIP by Jinya Liu (12705179)

“…Next, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO) enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were performed for functional enrichment analysis of these DEGs. Then, two machine learning algorithms, Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were used to get the diagnostic genes. …”

DataSheet1_LncRNA model predicts liver cancer drug resistance and validate in vitro experiments.CSV by Qiushi Yin (1661437)

“…Machine learning algorithms including random forest (RF), lasso regression (LR), and support vector machines (SVMs) were used to identify important chemotherapy-related LncRNAs. …”

DataSheet4_LncRNA model predicts liver cancer drug resistance and validate in vitro experiments.CSV by Qiushi Yin (1661437)

“…Machine learning algorithms including random forest (RF), lasso regression (LR), and support vector machines (SVMs) were used to identify important chemotherapy-related LncRNAs. …”

Image5_Identification and Validation of the Diagnostic Characteristic Genes of Ovarian Cancer by Bioinformatics and Machine Learning.TIF by Jinya Liu (12705179)

“…Next, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO) enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were performed for functional enrichment analysis of these DEGs. Then, two machine learning algorithms, Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were used to get the diagnostic genes. …”

Image1_LncRNA model predicts liver cancer drug resistance and validate in vitro experiments.PNG by Qiushi Yin (1661437)

“…Machine learning algorithms including random forest (RF), lasso regression (LR), and support vector machines (SVMs) were used to identify important chemotherapy-related LncRNAs. …”

Efficient GPU Enabled QM/MM Calculations: AMBER Coupled with GPU Enabled QUICK by Madushanka Manathunga (8383776)

“…The shortcoming of QM/MM models when using ab initio or density functional theory (DFT) methods is the computational expense, which limits QM/MM modeling. …”

Data_Sheet_2_A genome-wide association study of a rage-related misophonia symptom and the genetic link with audiological traits, psychiatric disorders, and personality.docx by Dirk J. A. Smit (7533146)

“…First, we used gene-based and functional annotation analyses to explore neurobiological determinants of the rage-related misophonia symptom. …”

Data Sheet 1_Integrative multi-omics identifies MEIS3 as a diagnostic biomarker and immune modulator in hypertrophic cardiomyopathy.docx by Jinchen He (18929662)

“…</p>Methods<p>We performed bulk RNA sequencing on peripheral blood samples from clinically diagnosed HCM patients (n = 4) and matched healthy controls (n = 3), followed by differential expression analysis and weighted gene co-expression network analysis (WGCNA). Machine learning algorithms (LASSO and Random Forest) were used to identify key diagnostic genes. …”

Data_Sheet_1_A genome-wide association study of a rage-related misophonia symptom and the genetic link with audiological traits, psychiatric disorders, and personality.docx by Dirk J. A. Smit (7533146)

“…First, we used gene-based and functional annotation analyses to explore neurobiological determinants of the rage-related misophonia symptom. …”

DataSheet_1_Deciphering Obesity-Related Gene Clusters Unearths SOCS3 Immune Infiltrates and 5mC/m6A Modifiers in Ossification of Ligamentum Flavum Pathogenesis.docx by Baoliang Zhang (1500805)

“…</p>Results<p>Ninety-nine ORDEGs were preliminarily identified, and functional annotations showed these genes were mainly involved in metabolism, inflammation, and immune-related biological functions and pathways. …”

Data_Sheet_2_Higher Sensitivity and Reproducibility of Wavelet-Based Amplitude of Resting-State fMRI.pdf by Fei-Fei Luo (6566588)

“…<p>The fast Fourier transform (FFT) is a widely used algorithm used to depict the amplitude of low-frequency fluctuation (ALFF) of resting-state functional magnetic resonance imaging (RS-fMRI). …”

Data_Sheet_1_Higher Sensitivity and Reproducibility of Wavelet-Based Amplitude of Resting-State fMRI.doc by Fei-Fei Luo (6566588)

“…<p>The fast Fourier transform (FFT) is a widely used algorithm used to depict the amplitude of low-frequency fluctuation (ALFF) of resting-state functional magnetic resonance imaging (RS-fMRI). …”

Data for Reinforcement Learning-based Congestion Control: A Systematic Evaluation of Fairness, Efficiency and Responsiveness by Luca Giacomoni (4466608)

“…Depending on the experiment, a different seed may be used. However, in most of the cases, all runs are identical.…”

Data_Sheet_1_Machine Learning Models to Predict Cognitive Impairment of Rodents Subjected to Space Radiation.docx by Mona Matar (11424043)

“…<p>This research uses machine-learned computational analyses to predict the cognitive performance impairment of rats induced by irradiation. …”