Additional file 1: of Investigating cellular network heterogeneity and modularity in cancer: a network entropy and unbalanced motif approach

Table S1. The statistics of RNA-Seq data for tumors and normal tissues in 8 cancer types collected from TCGA. Table S2. The statistics of RNA-Seq data for four stages of tumor progression in 6 cancer types collected from TCGA. Figure S1. Local network entropy distribution for cancer significantly mutated genes among four stages (I-IV) of tumor progression in 6 cancer types. Figure S2. Local network entropy distribution for Cancer Gene Census (CGC) genes among four stages (I-IV) of tumor progression in 6 cancer types. Figure S3. Local network entropy distribution for oncogenes (OGs) among four stages (I-IV) of tumor progression in 6 cancer types. Figure S4. Local network entropy distribution for tumor suppressor genes (TSGs) among four stages (I-IV) of tumor progression in 6 cancer types. Figure S5. Local network entropy distribution for 458 drug-sensitivity genes in drug sensitive versus resistant cancer cell lines. (DOCX 994 kb

Recall metric of the parameter <i>β</i> on the node weighted network-based inference method for test set when assessed the top five predicted candidate lists.

<p>The recall reaches its maximum value at about 0.4 and 0.3 for GPCRs (<b>A</b>) and kinases (<b>B</b>), respectively. The error bars denote the standard deviation by 10 times independent simulation test.</p

The performance of difference inference methods in the external validation set of GPCRs and kinases.

<p>All performances were evaluated based on top 20 predicted lists. NBI, network-based inference; NWNBI, node weighted network-based inference; EWNBI, edge weighted network-based inference; DBSI-T, drug-based similarity inference with Tanimoto similarity score; DBSI-C, DBSI with Cosine similarity score; DBSI-F, DBSI with Forbes similarity score; DBSI-R, DBSI with Russell-rao similarity score; TBSI, target-based similarity inference; R, recall; ER, recall enhancement; AUC, the area under the receiver operating characteristic curve; C_i (P_a, P_b, …, P_m) represents the prioritization of new targets for a given chemical; P_j (C_a, C_b, …, C_n) represents the prioritization of new chemicals for a given protein.</p

Analysis of the role of weak chemical-protein interactions by exponent <i>λ</i>.

<p>When , it is unweighted NBI method; when , it is the EWNBI method. When , it positively strengthens the weighted value of strong CPI edges, while positively strengthens the weighted value of weak CPI edges. Otherwise, a negative will give the negative effects. The area under receiver operating characteristic curve (AUC) was yielded for test set by simulation 10 times test, the error bar denotes the standard deviation. GPCRs (<b>A</b>) and kinases (<b>B</b>).</p

Discovered chemical-protein interactions (CPI) bipartite networks among 267 FDA approved or experimental drugs and 130 kinases.

<p>Circle and square nodes correspond to drugs and kinases, respectively. A gray line represents the old CPI annotated in the DrugBank and KEGG. The red line represents the predicted CPI. The red arrow line represents the new predicted CPI which is validated by literatures. The size of the drug node is the fraction of the number of targets that the drug linked. The size of the target node is the fraction of the number of drugs that the target linked. Color codes are given in the legend. Drug nodes (circles) are colored according to their Anatomical Therapeutic Chemical Classification. This graph and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041064#pone-0041064-g006" target="_blank">Figure 6</a> were prepared by Cytoscape (<a href="http://www.cytoscape.org/" target="_blank">http://www.cytoscape.org/</a>).</p

The performance of the test set of GPCRs and kinases using different methods by 10 simulation times test of 10-fold cross validation.

<p>All performances were evaluated based on top 5 predicted lists. NBI, network-based inference; NWNBI, node weighted network-based inference; EWNBI, edge weighted network-based inference; DBSI-T, drug-based similarity inference with Tanimoto similarity score; DBSI-C, DBSI with Cosine similarity score; DBSI-F, DBSI with Forbes similarity score; DBSI-R, DBSI with Russell-rao similarity score; TBSI, target-based similarity inference; R, recall; ER, recall enhancement; AUC, the area under the receiver operating characteristic curve; C_i (P_a, P_b, …, P_m) represents the prioritization of new targets for a given chemical; P_j (C_a, C_b, …, C_n) represents the prioritization of new chemicals for a given protein. *The standard deviation of the performance measured by 10 independent simulation times test of 10-fold cross validation.</p

Schematic diagram of our proposed method.

<p>(<b>A</b>) The drug-based similarity inference (DBSI), (<b>B</b>) the target-based similarity inference (TBSI) and (<b>C</b>) the unweighted network-based inference (NBI), (<b>D</b>) the edge-weighted NBI (EWNBI) and (<b>E</b>) the node-weighted NBI (NWNBI). Green circle: chemical node, gold square: protein node, black line: unweighted interaction link, cyan line: chemical-chemical two-dimensional structural similarity (S_c) or protein-protein Smith Waterman genomic similarity (S_g), red line: weighted edges (thick red line denotes the strong edge with high potency and thin red line denotes the weak edge with low potency).</p

Box plots of compound-compound and protein-protein similarities against compound or protein structure activity-relationship (SAR) similarities.

<p>(<b>A</b>) protein-protein (GPCRs) sequence similarity (Smith-Waterman scores) against GPCRs SAR similarity, (<b>B</b>) protein-protein (kinases) sequence similarities (Smith-Waterman scores) against kinases SAR similarity, (<b>C</b>) compound-compound (GPCR ligands) structural similarities (Tanimoto scores) against the GPCR ligands SAR similarities and (<b>D</b>) compound-compound (kinase ligands) structural similarity (Tanimoto scores) against kinase ligands SAR similarities.</p

Le Cri du peuple : journal politique quotidien

08 avril 19141914/04/08 (A26,N22)-1914/04/08

Discovered chemical-protein interaction (CPI) bipartite network among 139 FDA approved or experimental drugs and 55 GPCRs (Table S4).

<p>Circle and square nodes correspond to drugs and GPCRs, respectively. The definition of nodes and edges were given in the caption of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041064#pone-0041064-g005" target="_blank">Figure 5</a>.</p