Top scoring ASD network genes.

<p>List of the 14 top scoring ASD network genes, present in both ASD networks and in none of the other disorders (ASD specificity score = 1), with information on gene-wise association <i>P</i>-value and biological processes relevant for ASD.</p><p>NNP - No neurological phenotypes|; NA - No mouse model available.</p><p>Top scoring ASD network genes.</p

ASD top scoring gene network.

<p>This network illustrates the 14 top scoring genes included in the ASD LCC and their first neighbors. Nodes are colored based on a score reflecting their presence in the second ASD dataset and in the 6 unrelated diseases LCCs. A darker color represents a higher score, which means a higher specificity for ASD.</p

Workflow of the strategy for network definition, validation and identification of most relevant candidate genes.

<p>Workflow of the strategy for network definition, validation and identification of most relevant candidate genes.</p

Network properties of proteins selected at gene-wise <i>P</i><0.1 in each ASD.

<p><b>a</b>) Comparison of percentage of direct interactions and isolated nodes between proteins selected at gene-wise <i>P</i><0.1 in each GWAS dataset (red circles) vs 1000 random samples of network proteins (represented by light gray and dark gray box plots, for direct interactions and isolated nodes, respectively). The bottom and top of the box represent the 25th and 75th percentile and the extremity of the whiskers the maximum and minimum of the random samples data. <b>b</b>) Same comparison for the largest connected component (LCC) size.</p

Precision and recall were consistently higher for LCC genes relative to top GWAS genes or genes selected at <i>P</i><0.1.

<p>Precision and Recall (Percentage), by ASD dataset, of three sets of genes (genes selected at a gene wise <i>P</i>-value cutoff of 0.1, genes included in the LCC and the same number of GWAS top genes) against a list of known disease candidates.</p><p>Precision and recall were consistently higher for LCC genes relative to top GWAS genes or genes selected at <i>P</i><0.1.</p

The architecture of the automated workflow for predicting disease genes.

Functional similarity layer is the instantiation of methodology step 1, while the classifier layer implements the steps 2 and 3 of proposed methodology (Fig 1).</p

Classifying ASD database genes into HD genes and LD genes using the available evidence strength.

Classifying ASD database genes into HD genes and LD genes using the available evidence strength.</p

Graphical representation of the methodology to predict ASD genes.

A. ASD genes with different level of evidence and non-mental genes were used to implement the proposed methodology. B. Three different semantic similarity measures were used to calculate functional similarities for HD + non-mental genes and for HD + LD + non-mental genes. C. Four different machine learning methods were used to analyze the computed gene functional similarities. Machine learning classifiers were tested using stratified and held-out restricted stratified five-fold cross-validation. Like the Krishnan et al. method in the held-out restricted validation, only HD + non-mental genes were chosen for testing the classifier. In stratified five-fold cross-validation, classifiers were evaluated using all genes in the test set.</p

Main components of the proposed methodology to predict disease genes.

Functional similarities are computed for a given set of genes. Different machine learning methods are applied to functional similarity matrices to define rules that discriminate disease genes from non-disease genes. Two evaluation approaches, namely stratified and held-out restricted stratified five-fold cross-validation are used.</p

Top 16 enriched HPO terms for predicted ASD genes.

Top 16 enriched HPO terms for predicted ASD genes.</p