Table_5_Algorithm-Based Meta-Analysis Reveals the Mechanistic Interaction of the Tumor Suppressor LIMD1 With Non-Small-Cell Lung Carcinoma.xlsx by Ling Wang (56577)

“…In this study, we have carried out multiple genome-wide bioinformatic analyses for a comprehensive understanding of LIMD1 in NSCLC, using various online algorithm platforms that have been built for mega databases derived from both clinical and cell line samples. …”

Antihypertensive treatment algorithm. by Kira Bang Bove (9607123)

Cell model. by Qi Song (121209)

Subjects:

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). …”

Image 1_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). …”

Table 1_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). …”

Algorithm and classification of groups. by Ming-Chun Yang (6729968)

Results of DE algorithms with our scheme and original DE algorithms when function dimensionality is set 100. by Haiming Du (6691646)

Results of DE algorithms with our scheme and original DE algorithms when function dimensionality is set 30. by Haiming Du (6691646)

Results of DE algorithms with our scheme and original DE algorithms when function dimensionality is set 50. by Haiming Du (6691646)

The image of the Aekley function. by Zhengyu Xu (8550660)

Discovery of Protein Modifications Using Differential Tandem Mass Spectrometry Proteomics by Paolo Cifani (1575613)

“…Termed SAMPEI for spectral alignment-based modified peptide identification, this open-source algorithm is designed for the discovery of functional protein and peptide signaling modifications, without prior knowledge of their identities. …”

Discovery of Protein Modifications Using Differential Tandem Mass Spectrometry Proteomics by Paolo Cifani (1575613)

“…Termed SAMPEI for spectral alignment-based modified peptide identification, this open-source algorithm is designed for the discovery of functional protein and peptide signaling modifications, without prior knowledge of their identities. …”

The skeleton algorithm identifies mitochondrial density in sample images from cells. by Margarita M. Ivanova (11604141)

“trend+var” algorithm. by Roman Olson (6577004)

ROC analysis of the Machine Learning Algorithm. by Stephan Ellmann (314565)

Subjects: “…Cell Biology…”

The ALO algorithm optimization flowchart. by Wenjing Wang (181404)

“…The average running time of the proposed algorithm in Sphere function and Griebank function was 2.67s and 1.64s, respectively. …”