Comparison of Tanimoto with the combination of mimMiner and Tanimoto in LOOCV.

<p>Comparison of Tanimoto with the combination of mimMiner and Tanimoto in LOOCV.</p

LOOCV results for 913 monogenic diseases, based on symmetric disease phenotype networks processed with the logistic regression function.

<p>LOOCV results for 913 monogenic diseases, based on symmetric disease phenotype networks processed with the logistic regression function.</p

Best <i>C</i> values in different DPNs.

<p>Best <i>C</i> values in different DPNs.</p

Comparison of the LOOCV best results for 165 polygenic diseases, based on Tanimoto, mimMiner and their combination.

<p>The prioritization performance of Tanimoto, mimMiner and their combination is compared based on the evaluation criteria including mean rank ratio (MRR), number of the top-ranking genes, and True Positive Rate (TPR) in the top 5, 10 and 30 ranked genes. Totally, there were 427 disease-gene pairs in the 165 polygenic diseases. For the number of top-ranking genes shown in this figure, its percentage in 427 genes (i.e. TPR in the top 1) was calculated.</p

LOOCV results for 165 polygenic diseases, based on seed genes and different disease phenotype networks processed with the logistic regression function.

<p>LOOCV results for 165 polygenic diseases, based on seed genes and different disease phenotype networks processed with the logistic regression function.</p

LOOCV results for 913 monogenic diseases, based on original disease phenotype networks.

<p>LOOCV results for 913 monogenic diseases, based on original disease phenotype networks.</p

LOOCV results for 913 monogenic diseases, based on asymmetric Tanimoto disease phenotype networks processed with the logistic regression function.

<p>LOOCV results for 913 monogenic diseases, based on asymmetric Tanimoto disease phenotype networks processed with the logistic regression function.</p

Correlation between parameter <i>C</i> of the logistic function and the number of top-ranking genes.

<p>Correlation curve of parameter <i>C</i> of the logistic function and the number of top-ranking genes is plotted, and the slow peak value is presented for each of the six disease phenotype networks. The combination is an integration of mimMiner and Tanimoto in the respective proportion of 50%.</p

Comparison of the mimMiner with the combination of mimMiner and Tanimoto in LOOCV.

<p>Comparison of the mimMiner with the combination of mimMiner and Tanimoto in LOOCV.</p

Flow-chart depicting the four steps of evaluating disease phenotype networks for gene prioritization.

<p>The first step consists of extracting the common disease records from mimMiner and resnikHPO to construct the corresponding subnetworks for comparative analysis. The resnikHPO has asymmetric and symmetric versions so that two subnetworks are extracted. Every subnetwork can be represented by an adjacency matrix. Step 2 consists of normalization and logistic regression. The asymmetric and symmetric subnetworks of resnikHPO need normalization to adjust all values into the range [0, 1]. Four methods shown in italics are adopted to process the corresponding adjacency matrix. Illustrated by the example of Lin method, the normalized asymmetric matrix is named Lin-R and Lin-C, while the normalized symmetric matrix is named Lin. In essence, Lin-R and Lin-C is the same matrix, but the difference is that the similarity scores between a given disease and the others are drawn from the row and column vectors of the matrix, respectively. In addition, mimMiner and resnikHPO can be integrated herein and the combination of mimMiner and Tanimoto outperforms each network alone (see the main text). Logistic regression is an optional process, but it substantially improves performance of the networks. Step 3 consists of validation and evaluation. Together with disease similarity as prior knowledge, 913 monogenetic diseases and 165 polygenetic diseases are input for gene prioritization in leave-one-out cross-validation (LOOCV) based on PRINCE algorithm using PPI network. Step 4 consists of case studies, involving gene prioritization for OMIM 102200 without seed genes and OMIM 114480 (Breast Cancer) with seed genes.</p