Example of the optimisation algorithm. by Laurens H. J. Krah (5823848)

Illustration of the BRIDES algorithm and the selection process. by Cindy Bouchard (6844979)

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Data_Sheet_1_Application of a maximal-clique based community detection algorithm to gut microbiome data reveals driver microbes during influenza A virus infection.pdf by Anirban Bhar (458682)

Simultaneous Estimation of Many Sparse Networks via Hierarchical Poisson Log-Normal Model by Changhao Ge (22410399)

Summary of the BRIDES node selection procedure providing the minimum number of nodes required to reach a maximal score, and the number of multiple solutions with the same score for... by Cindy Bouchard (6844979)

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Sampled population parameters and corresponding node-based metrics, node removal statistics and BRIDES results. by Cindy Bouchard (6844979)

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Population graph of 19 wood turtle populations based on conditional genetic distance (cGD). by Cindy Bouchard (6844979)

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Different population subgraphs based on distinct node selection parameters using a proactive or reactive conservation approach. by Cindy Bouchard (6844979)

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BRIDES node selection process for scenario Ap and Model B³D¹E²S³ (3, 0, 0, 1, 2, 3). by Cindy Bouchard (6844979)

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Results of the BRIDES node selection procedure for two scenarios and three weighted models. by Cindy Bouchard (6844979)

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Identification and functional analysis of hub genes. by Wei Ya Lan (22403712)

Flowchart of homolog collection performed by the PhyloFisher Python script <i>fisher</i>.<i>py</i>. by Alexander K. Tice (3137670)

Identification, validation, and functional analyses of hub immune-related genes. by Chengjun Yu (12136227)

“…(D) Venn diagram of intersection of top 10 hub genes calculated by Betweenness Centrality, Maximal Clique Centrality, and random forest algorithm. …”

Raw Data for the Thesis: "<i>Enhancing RNAi-Based Pest Control through Effective Target Gene Selection and Optimal dsRNA Design</i>" by Doga CEDDEN (12675286)

“…The chapter argues that unbiased, screen-based approaches outperform hypothesis-driven gene selection due to the unpredictable influence of cellular processes on RNAi efficacy. …”

Table_1_Generating Ensembles of Gene Regulatory Networks to Assess Robustness of Disease Modules.XLSX by James T. Lim (9984869)

“…However, these networks are not static, and during phenotypic transitions like disease onset, they can acquire new “communities” (or highly interacting groups) of genes that carry out cellular processes. Disease communities can be detected by maximizing a modularity-based score, but since biological systems and network inference algorithms are inherently noisy, it remains a challenge to determine whether these changes represent real cellular responses or whether they appeared by random chance. …”

Image_7_Generating Ensembles of Gene Regulatory Networks to Assess Robustness of Disease Modules.TIF by James T. Lim (9984869)

“…However, these networks are not static, and during phenotypic transitions like disease onset, they can acquire new “communities” (or highly interacting groups) of genes that carry out cellular processes. Disease communities can be detected by maximizing a modularity-based score, but since biological systems and network inference algorithms are inherently noisy, it remains a challenge to determine whether these changes represent real cellular responses or whether they appeared by random chance. …”

Image_2_Generating Ensembles of Gene Regulatory Networks to Assess Robustness of Disease Modules.TIF by James T. Lim (9984869)

“…However, these networks are not static, and during phenotypic transitions like disease onset, they can acquire new “communities” (or highly interacting groups) of genes that carry out cellular processes. Disease communities can be detected by maximizing a modularity-based score, but since biological systems and network inference algorithms are inherently noisy, it remains a challenge to determine whether these changes represent real cellular responses or whether they appeared by random chance. …”

Image_5_Generating Ensembles of Gene Regulatory Networks to Assess Robustness of Disease Modules.TIF by James T. Lim (9984869)

“…However, these networks are not static, and during phenotypic transitions like disease onset, they can acquire new “communities” (or highly interacting groups) of genes that carry out cellular processes. Disease communities can be detected by maximizing a modularity-based score, but since biological systems and network inference algorithms are inherently noisy, it remains a challenge to determine whether these changes represent real cellular responses or whether they appeared by random chance. …”

Image_6_Generating Ensembles of Gene Regulatory Networks to Assess Robustness of Disease Modules.TIF by James T. Lim (9984869)

“…However, these networks are not static, and during phenotypic transitions like disease onset, they can acquire new “communities” (or highly interacting groups) of genes that carry out cellular processes. Disease communities can be detected by maximizing a modularity-based score, but since biological systems and network inference algorithms are inherently noisy, it remains a challenge to determine whether these changes represent real cellular responses or whether they appeared by random chance. …”

Image_4_Generating Ensembles of Gene Regulatory Networks to Assess Robustness of Disease Modules.TIF by James T. Lim (9984869)

“…However, these networks are not static, and during phenotypic transitions like disease onset, they can acquire new “communities” (or highly interacting groups) of genes that carry out cellular processes. Disease communities can be detected by maximizing a modularity-based score, but since biological systems and network inference algorithms are inherently noisy, it remains a challenge to determine whether these changes represent real cellular responses or whether they appeared by random chance. …”