Search alternatives:
based optimization » whale optimization (Expand Search)
primary case » primary cause (Expand Search), primary care (Expand Search), primary causes (Expand Search)
case based » made based (Expand Search), game based (Expand Search), rate based (Expand Search)
based optimization » whale optimization (Expand Search)
primary case » primary cause (Expand Search), primary care (Expand Search), primary causes (Expand Search)
case based » made based (Expand Search), game based (Expand Search), rate based (Expand Search)
-
141
Table_2_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX
Published 2019“…Secondly, we also integrated heterogonous omics data from TCGA datasets and GEO datasets including bulk RNA-seq data, which outperformed the other methods at identifying distinct cancer subtypes. Within an additional case study, we also constructed the mRNA-miRNA regulatory network of colorectal cancer based on the feature weight estimated from HCI, where the differentially expressed mRNAs and miRNAs were significantly enriched in well-known functional sets of colorectal cancer, such as KEGG pathways and IPA disease annotations. …”
-
142
Table_1_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.docx
Published 2019“…Secondly, we also integrated heterogonous omics data from TCGA datasets and GEO datasets including bulk RNA-seq data, which outperformed the other methods at identifying distinct cancer subtypes. Within an additional case study, we also constructed the mRNA-miRNA regulatory network of colorectal cancer based on the feature weight estimated from HCI, where the differentially expressed mRNAs and miRNAs were significantly enriched in well-known functional sets of colorectal cancer, such as KEGG pathways and IPA disease annotations. …”
-
143
Table_3_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLS
Published 2019“…Secondly, we also integrated heterogonous omics data from TCGA datasets and GEO datasets including bulk RNA-seq data, which outperformed the other methods at identifying distinct cancer subtypes. Within an additional case study, we also constructed the mRNA-miRNA regulatory network of colorectal cancer based on the feature weight estimated from HCI, where the differentially expressed mRNAs and miRNAs were significantly enriched in well-known functional sets of colorectal cancer, such as KEGG pathways and IPA disease annotations. …”
-
144
Table_5_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX
Published 2019“…Secondly, we also integrated heterogonous omics data from TCGA datasets and GEO datasets including bulk RNA-seq data, which outperformed the other methods at identifying distinct cancer subtypes. Within an additional case study, we also constructed the mRNA-miRNA regulatory network of colorectal cancer based on the feature weight estimated from HCI, where the differentially expressed mRNAs and miRNAs were significantly enriched in well-known functional sets of colorectal cancer, such as KEGG pathways and IPA disease annotations. …”
-
145
Image_3_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF
Published 2021“…</p><p>Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations.…”
-
146
Image_5_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF
Published 2021“…</p><p>Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations.…”
-
147
Table_3_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.docx
Published 2021“…</p><p>Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations.…”
-
148
Table_1_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.docx
Published 2021“…</p><p>Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations.…”
-
149
Image_1_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF
Published 2021“…</p><p>Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations.…”
-
150
Image_2_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF
Published 2021“…</p><p>Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations.…”
-
151
Image_6_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF
Published 2021“…</p><p>Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations.…”
-
152
Table_2_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.docx
Published 2021“…</p><p>Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations.…”
-
153
Image_4_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF
Published 2021“…</p><p>Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations.…”