NemoSuite: Web-based Network Motif Analytic Suite

Kim, Wooyoung; Hsu, Yi-Hsin; Li, Zican; Mar, Preston; Wang, Yangxiao

doi:10.25046/aj0506185

Cited by 3 publications

(3 citation statements)

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“…The web program NemoSuite (Network Motif Analysis in a Suite; https://bioresearch. css.uwb.edu/biores/NemoSuite/) accessed on 23 August 2023 developed by Kim et al [32] was applied to detect and analyze network motifs. Although network motifs may provide deep insight into the network's functional abilities, their detection is computationally challenging and not well supported by existing systems.…”

Section: Network Motif Analysismentioning

confidence: 99%

“…Although network motifs may provide deep insight into the network's functional abilities, their detection is computationally challenging and not well supported by existing systems. NemoSuite is an easily accessible and highly usable tool that is available in a web-interactive and graphical user interface format [32]. It offers great efficiency that current network motif detection software programs lack, such as various input and output options, enabling users to find efficient solutions for motif detection.…”

Section: Network Motif Analysismentioning

confidence: 99%

“…An "undirected graph" was chosen, given the cross-sectional nature of the study design. The motif size (k) was set to four, considering the computational efficiency, interpretability, and statistical robustness, and the random graph size was set to 10 [32]. A network-centric approach offers three output options-NemoCount, Nemo-Profile, and NemoCollection-whose formats were introduced by Kim and Haukap [34].…”

Section: Network Motif Analysismentioning

confidence: 99%

See 2 more Smart Citations

Network Motif Detection in the Network of Inflammatory Markers and Depression Symptoms among Patients with Stable Coronary Heart Disease: Insights from the Heart and Soul Study

Lee,

Whooley,

Niitsu

et al. 2024

Psychology International

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Background. Network motif analysis is a technique used to explore recurrent and statistically significant subgraphs within a network. Applying a motif analysis to the complex network of inflammation and depression may yield nuanced insight into the specific interaction mechanisms between inflammatory markers and individual depression symptoms, which is our aim. Methods. This cross-sectional study is based on patients with stable coronary heart disease (CHD). A partial correlation network was initially constructed to link inflammatory markers, including C-reactive protein (CRP), Interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), monocyte chemoattractant protein-1 (MCP-1), individual depression symptoms, and covariates. A network-centric approach searched all possible non-isomorphic subgraph patterns of size k = 4 in the network. Results. Although CRP, IL-6, and TNF-α displayed an insignificant association with specific depression symptoms, the motif analysis revealed various subgraph patterns of interactions between depression symptoms associated with MCP-1. Generally, MCP-1 formed a closed loop with psychomotor problems and sleep disturbances, and this configuration was connected in various forms with other symptoms, particularly cognitive (e.g., feelings of worthlessness, concentration difficulty, and suicidal ideation) and neurovegetative/somatic (e.g., appetite changes and fatigue) symptoms. Moreover, MCP-1 was frequently associated with a closed-loop triangle comprising cognitive and neurovegetative/somatic symptoms but not with mood symptoms (e.g., loss of interest and feelings of sadness). Conclusions. The findings provide insight into how MCP-1 may be involved in the pathology of depression among patients with stable CHD in a more precise manner. This study also proposes future directions for research on depression.

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Section: Network Motif Analysismentioning

confidence: 99%

Section: Network Motif Analysismentioning

confidence: 99%