SUMMARY: It has been observed in different kinds of networks, such as social or biological ones, a typical behavior inspired by the general principle 'similarity breeds connections'. These networks are defined as homophilic as nodes belonging to the same class preferentially interact with each other. In this work, we present HONTO (HOmophily Network TOol), a user-friendly open-source Python3 package designed to evaluate and analyze homophily in complex networks. The tool takes in input from the network along with a partition of its nodes into classes and yields a matrix whose entries are the homophily/heterophily z-score values. To complement the analysis, the tool also provides z-score values of nodes that do not interact with any other node of the same class. Homophily/heterophily z-scores values are presented as a heatmap allowing a visual at-a-glance interpretation of results. AVAILABILITY AND IMPLEMENTATION: Tool's source code is available at https://github.com/cumbof/honto under the MIT license, installable as a package from PyPI (pip install honto) and conda-forge (conda install -c conda-forge honto), and has a wrapper for the Galaxy platform available on the official Galaxy ToolShed (Blankenberg et al., 2014) at https://toolshed.g2.bx.psu.edu/view/fabio/honto.

Abstract Transcription factors are proteins able to selectively bind DNA short traits, namely transcription factors binding sites, in order to regulate gene expression in terms of both repression and activation. Despite plenty of studies focusing on transcription factors and on the role they play in specific biological tasks or diseases, is available in the literature, to our knowledge there is no tool able to automatically provide a list of transcription factors involved in this task and the associated interaction network through a solid computational analysis. TRANScriPtion fActor REgulatory NeTwork (TRANSPARENT) is a user-friendly Python tool designed to help researchers in studying given biological tasks or given diseases in human, by identifying transcription factors controlling and regulating the expression of genes associated with that task or disease. The tool takes in input a list of gene IDs and provides (1) a set of transcription factors that are significantly associated with the input genes, (2) the correspondent P values (i.e., the probability that this observed association was driven by chance) and (3) a transcription factor network that can be directly visualized through STRING database. The effectiveness and reliability of the tool were assessed by applying it to two different test cases: schizophrenia and autism disorders. The obtained results clearly show that identified TFs, for both datasets, are significantly associated with those disorders, in terms of both gene enrichment and coherence with the literature. The proposed tool TRANSPARENT can be a useful instrument to investigate transcription factor networks and unveil the role that TFs play in given biological tasks and diseases.

In this article, we propose a novel approach for measuring word association based on the joint occurrences distribution in a text. Our approach relies on computing a sum of distances between neighboring occurrences of a given word pair and comparing it with a vector of randomly generated occurrences. The idea behind this assumption is that if the distribution of co‐occurrences is close to random or if they tend to appear together less frequently than by chance, such words are not semantically related. We devise a distance function S that evaluates the words association rate. Using S, we build a concept tree, which provides a visual and comprehensive representation of keywords association in a text. In order to illustrate the effectiveness of our algorithm, we apply it to three different texts, showing the consistency and significance of the obtained results with respect to the semantics of documents. Finally, we compare the results obtained by applying our proposed algorithm with the ones achieved by both human experts and the co‐occurrence correlation method. We show that our method is consistent with the experts' evaluation and outperforms with respect to the co‐occurrence correlation method.

Two T helper (Th) cell subsets, namely Th1 and Th2 cells, play an important role in inflammatory diseases. The two subsets are thought to counter-regulate each other, and alterations in their balance result in different diseases. This paradigm has been challenged by recent clinical and experimental data. Because of the large number of genes involved in regulating Th1 and Th2 cells, assessment of this paradigm by modeling or experiments is difficult. Novel algorithms based on formal methods now permit the analysis of large gene regulatory networks. By combining these algorithms with in silico knockouts and gene expression microarray data from human T cells, we examined if the results were compatible with a counter-regulatory role of Th1 and Th2 cells. We constructed a directed network model of genes regulating Th1 and Th2 cells through text mining and manual curation. We identified four attractors in the network, three of which included genes that corresponded to Th0, Th1 and Th2 cells. The fourth attractor contained a mixture of Th1 and Th2 genes. We found that neither in silico knockouts of the Th1 and Th2 attractor genes nor gene expression microarray data from patients with immunological disorders and healthy subjects supported a counter-regulatory role of Th1 and Th2 cells. By combining network modeling with transcriptomic data analysis and in silico knockouts, we have devised a practical way to help unravel complex regulatory network topology and to increase our understanding of how network actions may differ in health and disease.

MOTIVATION: An unbalanced differentiation of T helper cells from precursor type TH0 to the TH1 or TH2 phenotype in immune responses often leads to a pathological condition. In general, immune reactions biased toward TH1 responses may result in auto-immune diseases, while enhanced TH2 responses may cause allergic reactions. The aim of this work is to integrate a gene network of the TH differentiation in an agent-based model of the hyper-sensitivity reaction. The implementation of such a system introduces a second level of description beyond the mesoscopic level of the inter-cellular interaction of the agent-based model. The intra-cellular level consists in the cell internal dynamics of gene activation and transcription. The gene regulatory network includes genes-related molecules that have been found to be involved in the differentiation process in TH cells. RESULTS: The simulator reproduces the hallmarks of an IgE-mediated hypersensitive reaction and provides an example of how to combine the mesoscopic level description of immune cells with the microscopic gene-level dynamics. AVAILABILITY: The basic version of the simulator of the immune response can be downloaded here: http://www.iac.cnr.it/~filippo/C-ImmSim.html