The algorithm is designed to identify patterns in the treatment dataset. by Belén Otero-Carrasco (21262360)

Core genes were selected through PPI analysis based on three algorithms. by Dong-Hee Han (140305)

“…<b>(B)</b> The priority of the top 10 genes was evaluated through MNC, which identifies clusters of protein nodes that are more functionally connected to each other and selects the central proteins within the cluster. …”

Supplementary Tables 1 - 7 from A Meta-analysis of Lung Cancer Gene Expression Identifies <i>PTK7</i> as a Survival Gene in Lung Adenocarcinoma by Ron Chen (14910750)

Subjects:

Table3_Identification of Novel Lung Cancer Driver Genes Connecting Different Omics Levels With a Heat Diffusion Algorithm.XLSX by Fei Yuan (24924)

“…From the driver genes at each level, the Laplacian heat diffusion algorithm was executed on a protein–protein interaction network for discovering latent driver genes at this level. …”

Table4_Identification of Novel Lung Cancer Driver Genes Connecting Different Omics Levels With a Heat Diffusion Algorithm.XLSX by Fei Yuan (24924)

“…From the driver genes at each level, the Laplacian heat diffusion algorithm was executed on a protein–protein interaction network for discovering latent driver genes at this level. …”

Table2_Identification of Novel Lung Cancer Driver Genes Connecting Different Omics Levels With a Heat Diffusion Algorithm.XLSX by Fei Yuan (24924)

“…From the driver genes at each level, the Laplacian heat diffusion algorithm was executed on a protein–protein interaction network for discovering latent driver genes at this level. …”

Table1_Identification of Novel Lung Cancer Driver Genes Connecting Different Omics Levels With a Heat Diffusion Algorithm.XLSX by Fei Yuan (24924)

“…From the driver genes at each level, the Laplacian heat diffusion algorithm was executed on a protein–protein interaction network for discovering latent driver genes at this level. …”

Unraveling the gender-specific molecular landscape of lung squamous cell carcinoma progression by Ayushi Dwivedi (20658343)

Fitting results of the different optimization algorithms applied. by Sonja Langthaler (6069149)

Image1_Identification of Six Prognostic Genes in EGFR–Mutant Lung Adenocarcinoma Using Structure Network Algorithms.JPEG by Haomin Zhang (4427188)

Table3_Identification of Six Prognostic Genes in EGFR–Mutant Lung Adenocarcinoma Using Structure Network Algorithms.XLSX by Haomin Zhang (4427188)

Table2_Identification of Six Prognostic Genes in EGFR–Mutant Lung Adenocarcinoma Using Structure Network Algorithms.XLSX by Haomin Zhang (4427188)

Image2_Identification of Six Prognostic Genes in EGFR–Mutant Lung Adenocarcinoma Using Structure Network Algorithms.JPEG by Haomin Zhang (4427188)

Table1_Identification of Six Prognostic Genes in EGFR–Mutant Lung Adenocarcinoma Using Structure Network Algorithms.XLSX by Haomin Zhang (4427188)

Pipeline of the interactor algorithm. by Jose Cleydson F. Silva (8685594)

Functional effect prediction of FUS mutations by ten nsSNP prediction algorithms. by L. F. S. Bonet (11502082)

Distribution of total lung capacity (TLC) in pulmonary function tests with discrepant interpretations between the algorithm and the clinicians. by Yuh-Chin T. Huang (17867207)

“…<p>Distribution of total lung capacity (TLC) in pulmonary function tests with discrepant interpretations between the algorithm and the clinicians.…”

IL18R1-Related Molecules as Biomarkers for Asthma Severity and Prognostic Markers for Idiopathic Pulmonary Fibrosis by Kun Lin (278765)

Subjects:

Standard benchmark functions used for the experimentation of EOSA and other similar optimization algorithms. by Tehnan I. A. Mohamed (16846175)

Functions of membership for <i>age</i> variable. by José Carlos R. Alcantud (6858155)