Using Network Medicine for identifying drug candidates for complex and infectious diseases.

miniPEB 2021: Network Analysis Link to the course: learnNetSci. Network Science: Overview Network Science is broadly employed in many fields: from understanding how friends bond in a party to how animals interact; from how superheroes appear in the same comic books to how genes can be related to a specific biological process. Network analysis is especially beneficial for understanding complex systems, in all research fields. Examples of complex biological or medical systems include gene regulatory, ecological, and neuropsychology networks.

2nd Workshop of Advanced Bioinformatics: Network Medicine

Calculates network measures such as Largest Connected Component (LCC), Proximity, Separation, Jaccard Index, along with permutation, when needed.

Categorize links and nodes from multiple networks in 3 categories: Common links (alpha) specific links (gamma), and different links (beta). Also categorizes the links into sub-categories and groups. The package includes a visualization tool for the networks. More information about the methodology can be found at https://doi.org/10.1371/journal.pone.0240523

Using Network Science for understanding mental disorders.

It provides a list of genes associated to diseases (g2d$clean and g2d$complete) based on the following 4 publications (GS2D, Fontaine (2016) doi:10.18547/gcb.2016.vol2.iss1.e33, DisGeNET, Pinero (2016) doi:10.1093/nar/gkw943 Berto2016, Berto (2016) doi:10.3389/fgene.2016.00031 and PsyGeNET, Sacristan (2015) doi:10.1093/bioinformatics/btv301). Those lists were combined and manually curated to have matching disease names. When provided a list of gene names, it calculates the disease enrichment of the gene set. The enrichment is calculated using proportion test and Fisher’s exact test. Adjusted fdr p-values are returned alongside with p-values combined using the Fisher’s method.

Computes the Weighted Topological Overlap with positive and negative signs (wTO) networks given a data frame containing the mRNA count/ expression/ abundance per sample, and a vector containing the interested nodes of interaction (a subset of the elements of the full data frame). It also computes the cut-off threshold or p-value based on the individuals bootstrap or the values reshuffle per individual. It also allows the construction of a consensus network, based on multiple wTO networks. The package includes a visualization tool for the networks.

From constructing, combining and comparing co-expression networks