Description of test functions. by Lijie Sun (224007)

Accuracy of the Random forest algorithm for different values of test samples and different choice of random states. by Abeer Aljohani (18497914)

Subjects:

Accuracy of the Random forest algorithm for different values of test samples and different choice window indexes. by Abeer Aljohani (18497914)

Subjects:

Data Sheet 1_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.docx by Ming Xie (420493)

“…By intersecting the result of 3 machine learning algorithms and WGCNA, 3 feature genes were identified, including MRAP2, PPP1R14A, and PLN genes. …”

Hyper parameter settings for ML Algorithms. by R. S. Jayaram (22139155)

Optimization process of fitness function . by Yuehong Zhang (82779)

Screening facilitated the identification of diagnostic biomarkers using machine learning methods. by Junjie Su (368807)

Objective functions and solving methods for PVRP problems with time windows, multiple depots, and a heterogeneous fleet. by Alejandro Arenas-Vasco (19797133)

Variations of loss functions versus epoch number. by Xueliang Guo (4797057)

Performance of SVM model for various kernel functions. by Xueliang Guo (4797057)

Performance of ENQUIRE’s gene normalization algorithm. by Luca Musella (19986312)

Table 1_Integrating GWAS and machine learning for disease risk prediction in the Taiwanese Hakka population.xlsx by Jing-Hong Xiao (22780781)

“…After standard quality control, 295,589 SNPs were retained. Fourteen machine-learning algorithms were evaluated using SNPs selected through traditional GWAS filtering and refined via wrapper-based feature selection with a best-first search algorithm. …”

Phase stability at interfaces (dataset) by J Pitfield (21962924)

Analyses of Immune microenvironment and functions between m7G clusters. by Jiangli Zhao (2308330)

Overnight technician routing and scheduling problem with time windows and balanced workloads: a bi-objective zebra optimization algorithm by Abolfazl Gharaei (21803416)

“…As such, we introduce a new hybrid category of TRSPTW that combines centroid-based clustering, which is an unsupervised machine learning (ML) approach, with a bi-objective zebra optimisation algorithm (BOZOA). …”

Image 4_Multidisciplinary analysis of the prognosis and biological function of NUBPL in gastric cancer.tif by Luqian Liu (10814985)

“…Elevated NUBPL expression levels can impair the function of chemokines. Moreover, patients with lower NUBPL expression levels exhibit better responses to immunotherapy. …”

Image 3_Multidisciplinary analysis of the prognosis and biological function of NUBPL in gastric cancer.tif by Luqian Liu (10814985)

“…Elevated NUBPL expression levels can impair the function of chemokines. Moreover, patients with lower NUBPL expression levels exhibit better responses to immunotherapy. …”

Image 5_Multidisciplinary analysis of the prognosis and biological function of NUBPL in gastric cancer.tif by Luqian Liu (10814985)

“…Elevated NUBPL expression levels can impair the function of chemokines. Moreover, patients with lower NUBPL expression levels exhibit better responses to immunotherapy. …”

Image 6_Multidisciplinary analysis of the prognosis and biological function of NUBPL in gastric cancer.tif by Luqian Liu (10814985)

“…Elevated NUBPL expression levels can impair the function of chemokines. Moreover, patients with lower NUBPL expression levels exhibit better responses to immunotherapy. …”

Image 1_Multidisciplinary analysis of the prognosis and biological function of NUBPL in gastric cancer.tif by Luqian Liu (10814985)

“…Elevated NUBPL expression levels can impair the function of chemokines. Moreover, patients with lower NUBPL expression levels exhibit better responses to immunotherapy. …”