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

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

Transmission modelling functional block diagram. by Alyssa N. Sbarra (18460545)

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

Data Sheet 1_Simplified two-compartment neuron with calcium dynamics capturing brain-state specific apical-amplification, -isolation and -drive.pdf by Elena Pastorelli (7024235)

“…This work provides the computational community with a two-compartment spiking neuron model that supports the proposed forms of brain-state-specific activity. A machine learning evolutionary algorithm, guided by a set of fitness functions, selected parameters defining neurons that express the desired apical dendritic mechanisms. …”

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)

Design and implementation of DelaySSA. The input data for DelaySSA are reaction information, initial states, parameters and delays. Combining these into the simulation function, th... by Ziyan Jin (21021976)

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. …”

Image 2_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. …”

Table 3_Integrative modeling of malignant epithelial programs in EGFR-mutant LUAD via single-cell transcriptomics and multi-algorithm machine learning.xlsx by Weiran Zhang (411189)

“…Pseudotime trajectories were constructed using Monocle2, and branch-specific genes were extracted for functional analysis. Differentially expressed genes were integrated with TCGA bulk transcriptomic data, and ten machine learning algorithms were applied to construct the EGFR Mutation-Associated Malignant Epithelial Cell-Related Signature (EGFRmERS). …”

Table 2_Integrative modeling of malignant epithelial programs in EGFR-mutant LUAD via single-cell transcriptomics and multi-algorithm machine learning.xlsx by Weiran Zhang (411189)

“…Pseudotime trajectories were constructed using Monocle2, and branch-specific genes were extracted for functional analysis. Differentially expressed genes were integrated with TCGA bulk transcriptomic data, and ten machine learning algorithms were applied to construct the EGFR Mutation-Associated Malignant Epithelial Cell-Related Signature (EGFRmERS). …”

Image 3_Integrative modeling of malignant epithelial programs in EGFR-mutant LUAD via single-cell transcriptomics and multi-algorithm machine learning.tif by Weiran Zhang (411189)

“…Pseudotime trajectories were constructed using Monocle2, and branch-specific genes were extracted for functional analysis. Differentially expressed genes were integrated with TCGA bulk transcriptomic data, and ten machine learning algorithms were applied to construct the EGFR Mutation-Associated Malignant Epithelial Cell-Related Signature (EGFRmERS). …”

Image 2_Integrative modeling of malignant epithelial programs in EGFR-mutant LUAD via single-cell transcriptomics and multi-algorithm machine learning.tif by Weiran Zhang (411189)

“…Pseudotime trajectories were constructed using Monocle2, and branch-specific genes were extracted for functional analysis. Differentially expressed genes were integrated with TCGA bulk transcriptomic data, and ten machine learning algorithms were applied to construct the EGFR Mutation-Associated Malignant Epithelial Cell-Related Signature (EGFRmERS). …”