Additional file 1: of Context-specific functional module based drug efficacy prediction

Number of functional modules (TIF 161 kb

Cymbopogon goeringii Honda

原著和名: ヲガルカヤ科名: イネ科 = Gramineae採集地: 台湾 四重渓 (石門) (台湾省 四重渓 (石門))採集日: 1968/8/11採集者: 萩庭丈壽整理番号: JH028802国立科学博物館整理番号: TNS-VS-978802備考: DB作成協力会による補足あ

Additional file 3: of Context-specific functional module based drug efficacy prediction

Comparison Pearson correlation coefficient between our model and elastic net. It contains correlation coefficient of our model and elastic net in each drug. (XLSX 9 kb

CASCADE: a novel quasi all paths-based network analysis algorithm for clustering biological interactions-0

<p><b>Copyright information:</b></p><p>Taken from "CASCADE: a novel quasi all paths-based network analysis algorithm for clustering biological interactions"</p><p>http://www.biomedcentral.com/1471-2105/9/64</p><p>BMC Bioinformatics 2008;9():64-64.</p><p>Published online 29 Jan 2008</p><p>PMCID:PMC2253513.</p><p></p>des. The values for nodes , , , , , and are the same as node 's. Results for other nodes are not shown. Final identified clusters are delimited when the merging threshold 2.0 is used

CASCADE: a novel quasi all paths-based network analysis algorithm for clustering biological interactions-1

<p><b>Copyright information:</b></p><p>Taken from "CASCADE: a novel quasi all paths-based network analysis algorithm for clustering biological interactions"</p><p>http://www.biomedcentral.com/1471-2105/9/64</p><p>BMC Bioinformatics 2008;9():64-64.</p><p>Published online 29 Jan 2008</p><p>PMCID:PMC2253513.</p><p></p>des. The values for nodes , , , , , and are the same as node 's. Results for other nodes are not shown. Final identified clusters are delimited when the merging threshold 2.0 is used

Dynamics of Regulatory Networks in the Developing Mouse Retina

<div><p>Understanding gene regulation is crucial to dissect the molecular basis of human development and disease. Previous studies on transcription regulatory networks often focused on their static properties. Here we used retinal development as a model system to investigate the dynamics of regulatory networks that are comprised of transcription factors, microRNAs and other protein-coding genes. We found that the active sub-networks are topologically different at early and late stages of retinal development. At early stages, the active sub-networks tend to be highly connected, while at late stages, the active sub-networks are more organized in modular structures. Interestingly, network motif usage at early and late stages is also distinct. For example, network motifs containing reciprocal feedback regulatory relationships between two regulators are overrepresented in early developmental stages. Additionally, our analysis of regulatory network dynamics revealed a natural turning point at which the regulatory network undergoes drastic topological changes. Taken together, this work demonstrates that adding a dynamic dimension to network analysis can provide new insights into retinal development, and we suggest the same approach would likely be useful for the analysis of other developing tissues.</p> </div

Expression correlations between miRNAs and their predicted targets.

<p>The distributions for the predicted targets in TargetScan database (solid) and for the random targets (dot) are shown. The column plot represents the Z-values of each interval against 1000 miRNA target randomization on the right Y-axis.</p

Enrichment analysis on six Gene Ontology terms (biological processes).

<p>Enrichment analysis for the genes specifically expressed in six development time points. X-axis is the time points and Y axis is the e-value of the terms at each time point.</p

Network motif dynamics.

<p>Dynamics of two distinct patterns of network motif classes (A and B). Five network motifs are shown for each cluster. The network motifs are illustrated in the corresponding boxes. The expression of protein-coding genes (C), or miRNAs (D) cannot separate the early and late stages, while the Z-values for network motifs (E) show clear separation between early and late developmental stages.</p

Topological measures of the static and active networks of six time points.

<p>Out-degree is the number of regulated genes by a TF or miRNA. In-degree is the number of regulating TFs or miRNAs of a target gene. Clustering coefficient measures the inter-connectivity around a node. Average path length is the average length of all shortest paths among all node pairs. Betweenness is the average number of shortest paths between all node pairs passing through a node. Reachability is the fraction of nodes that can be reached from a node in the network. The mean and standard deviation (mean±SD) of 300 random networks for each time point are presented in Random Networks row. Examples of early and late time point active sub-networks are illustrated in the last row.</p