Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging حسب D. Andrew Brown (6584837)

Table3_Comprehensive analysis of the progression mechanisms of CRPC and its inhibitor discovery based on machine learning algorithms.XLSX حسب Zhen Wang (72451)

"…Weighted gene coexpression network analysis (WGCNA) and two machine learning algorithms were employed to identify potential biomarkers for CRPC. …"

Table2_Comprehensive analysis of the progression mechanisms of CRPC and its inhibitor discovery based on machine learning algorithms.XLSX حسب Zhen Wang (72451)

"…Weighted gene coexpression network analysis (WGCNA) and two machine learning algorithms were employed to identify potential biomarkers for CRPC. …"

Table1_Comprehensive analysis of the progression mechanisms of CRPC and its inhibitor discovery based on machine learning algorithms.XLSX حسب Zhen Wang (72451)

"…Weighted gene coexpression network analysis (WGCNA) and two machine learning algorithms were employed to identify potential biomarkers for CRPC. …"

DataSheet1_Comprehensive analysis of immune-related gene signature based on ssGSEA algorithms in the prognosis and immune landscape of hepatocellular carcinoma.ZIP حسب Liangliang Wang (580395)

"…Furthermore, Cox regression analysis revealed 49 DEGs that were associated with survival. Then, 19 DEGs were screened using the LASSO algorithm for IRGs construction and patients were classified into high- and low-risk groups. …"

Table1_Development and evaluation of a chronic kidney disease risk prediction model using random forest.xlsx حسب Krish Mendapara (18890758)

Image2_Development and evaluation of a chronic kidney disease risk prediction model using random forest.png حسب Krish Mendapara (18890758)

Image3_Development and evaluation of a chronic kidney disease risk prediction model using random forest.png حسب Krish Mendapara (18890758)

Image1_Development and evaluation of a chronic kidney disease risk prediction model using random forest.png حسب Krish Mendapara (18890758)

Assessing the effectiveness of a melanopsin-based signal for colour constancy - ICVS Presentation 2019 حسب Daniel Garside (3367640)

"…<br><br>Additionally, the distinct spectral profile and spatial configuration (providing a secondary mesh across the retina) might provide a signal which would allow for point-wise colour constancy transformation, without relying on scene-level properties such as those employed in algorithms based on a ‘grey-world’ assumption.<br><br>To explore this problem, spectral reflectance data of natural objects from the Vrhel+ dataset (Vrhel, Gershon, and Iwan 1994), spectral power distributions from the Hernández-Andrés+ dataset (Hernández-Andrés et al. 2001), and the CIE 2006 10-deg cone fundamentals (CIE 2006), were used to compute chromaticity co-ordinates for a large number of feasible natural colour signals in MB space. …"

Table1_Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis.docx حسب Haolong Zhang (13911013)

"…The optimal model was determined based on the AUC values derived from various algorithms. …"

Table1_Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis.docx حسب Zhang Haolong (17456619)

"…The optimal model was determined based on the AUC values derived from various algorithms. …"

Image1_Construction of a Support Vector Machine–Based Classifier for Pulmonary Arterial Hypertension Patients.tif حسب Zhenglu Shang (11739470)

"…Moreover, four optimal feature genes were screened from the DEGs by support vector machine–recursive feature elimination (SVM-RFE) algorithm (EPB42, IFIT2, FOSB, and SNF1LK). …"

DataSheet1_Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis.docx حسب Haolong Zhang (13911013)

"…The optimal model was determined based on the AUC values derived from various algorithms. …"

DataSheet1_Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis.docx حسب Zhang Haolong (17456619)

"…The optimal model was determined based on the AUC values derived from various algorithms. …"

Image_6_Immunological Value of Prognostic Signature Based on Cancer Stem Cell Characteristics in Hepatocellular Carcinoma.TIF حسب Qianhui Xu (10517717)

"…The stemness-related (greenyellow) modules with 175 hub genes were screened based on DEGs and WGCNA. A prognostic signature was established using LASSO analysis of prognostic hub genes to classify samples into two risk subgroups, which exhibited good prognostic performance. …"

Image_2_Immunological Value of Prognostic Signature Based on Cancer Stem Cell Characteristics in Hepatocellular Carcinoma.TIF حسب Qianhui Xu (10517717)

"…The stemness-related (greenyellow) modules with 175 hub genes were screened based on DEGs and WGCNA. A prognostic signature was established using LASSO analysis of prognostic hub genes to classify samples into two risk subgroups, which exhibited good prognostic performance. …"

Image_1_Immunological Value of Prognostic Signature Based on Cancer Stem Cell Characteristics in Hepatocellular Carcinoma.TIF حسب Qianhui Xu (10517717)

"…The stemness-related (greenyellow) modules with 175 hub genes were screened based on DEGs and WGCNA. A prognostic signature was established using LASSO analysis of prognostic hub genes to classify samples into two risk subgroups, which exhibited good prognostic performance. …"

Image_9_Immunological Value of Prognostic Signature Based on Cancer Stem Cell Characteristics in Hepatocellular Carcinoma.TIF حسب Qianhui Xu (10517717)

"…The stemness-related (greenyellow) modules with 175 hub genes were screened based on DEGs and WGCNA. A prognostic signature was established using LASSO analysis of prognostic hub genes to classify samples into two risk subgroups, which exhibited good prognostic performance. …"

Image_4_Immunological Value of Prognostic Signature Based on Cancer Stem Cell Characteristics in Hepatocellular Carcinoma.TIF حسب Qianhui Xu (10517717)

"…The stemness-related (greenyellow) modules with 175 hub genes were screened based on DEGs and WGCNA. A prognostic signature was established using LASSO analysis of prognostic hub genes to classify samples into two risk subgroups, which exhibited good prognostic performance. …"