Additional file 1 of Identification of large disjoint motifs in biological networks

Appendix 1. This appendix shows the algebraic derivation of number of embeddings for each three of the four basic building blocks (see Section 1). In addition, the appendix lists further experimental analysis. Appendix file is attached as PDF file. (ZIP 151 kb

Summary of the traversal process for a randomly picked state from unobserved Type 1 states.

<p>If the path starting from a ends at , , or , then all the states on this path are transient (Step 2(i) of Algorithm 1). If the path starting from ends at a state like then all the states on the path from to are transient (excluding ) and all the states on the cycle from to are steady.</p

The comparison of our algorithm with an existing method, Genysis [18], [19],on real and random networks.

1<p>We used a cut-off time of 24-hours and “-” indicates that the method could not find all steady states within this time. denotes seconds and denotes minutes.</p>2<p>Running time of our algorithm when 90% of the steady states are found with 90% confidence.</p>3<p>Running time of our algorithm when 80% of the steady states are found with 80% confidence.</p

States of a hypothetical network with three genes.

<p>The binary values correspond to activation levels of these genes. (a) The three states on the left are transient and of Type 1. The state with self loop is steady and Type 0. (b) The four states in simple loop are cyclic steady states and they are of Type 1. (c) The leftmost state is transient and Type 1. Even though only is of Type 2 (others are Type 1), the remaining five states create a complex loop, and thus they are transient.</p

Convergence of the estimators for the steady state profiles of the genes.

<p>These genes are a selected subset of the genes of <i>p53 network</i> of <i>Homo Sapiens </i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007992#pone.0007992-Ogata1" target="_blank">[46]</a>. Y-axis shows for each gene the fraction of steady states that the gene is in active state.</p

Heatmap using CCLE dataset for Balanced Accuracy of NBC using SVR.

<p>X-axis represents 10 different correlation thresholds, and y-axis represents 14 different drugs. Color intensity of the figure represents the BAC of each threshold and each drug combination.</p

Heatmap using CCLE dataset for Balanced Accuracy.

<p>X-axis represents 8 different drug types, and y-axis represents 4 different cancer types. Color intensity of the figure represents the BAC of each drug and each cancer combination. (A)NBC(SVR). (B)NBC(Ridge). (C)SVM(linear). (D)SVM(RBF). (E)RF. (F)GNB. (G)kNN. (H)Color Key.</p

Receiver operating characteristic (ROC) curves using CCLE data set.

<p>X-axis represents false positive rate, and y-axis represents true positive rate. (A)ROC curve for drug Lapatinib. (B)ROC curve for drug Erlotinib.</p

The relationship between prediction success rate of genes and their publication evidences for the top three drugs with the highest classification accuracy rate.

<p>The relationship between prediction success rate of genes and their publication evidences for the top three drugs with the highest classification accuracy rate.</p

List of resistant and sensitive cutoffs from the drug concentration values.

<p>List of resistant and sensitive cutoffs from the drug concentration values.</p