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Biology subjects

Cannistraci, C. V.

Publications and source records attributed to Cannistraci, C. V..

2 recordsLinked to original sources

Local-community network automata modelling based on length-three-paths for prediction of complex network structures in protein interactomes, food webs and more

From nests to nets intricate wiring diagrams surround the birth and the death of life. Here we show that the same rule of complex network self-organization is valid across different physical scales and allows to predict protein interactions, food web trophic relations and world trade network transitions. This rule, which we named CH2-L3, is a network automaton that is based on paths of length-three and that maximizes internal links in local communities and minimizes external ones, according to a mechanistic model essentially driven by topological neighbourhood information.

systems biology

LIPEA: Lipid Pathway Enrichment Analysis

MotivationAnalyzing associations among multiple omic variables to infer mechanisms that meaningfully link them is a crucial step in systems biology. Gene Set Enrichment Analysis (GSEA) was conceived to pursue this aim in computational genomics, unveiling significant pathways associated to certain gene signatures under investigation. Lipidomics is a rapidly growing omic field, and absolute quantification of lipid abundance by shotgun mass spectrometry is generating high-throughput datasets that depict lipid metabolism in a plethora of conditions and organisms. In addition, high-throughput lipidomics represents a new important ally to develop personalized medicine approaches, investigate the causes and predict effective biomarkers in metabolic diseases, and not only.\n\nResultsHere, we present Lipid Pathway Enrichment Analysis (LIPEA), a web-tool for over-representation analysis of lipid signatures and detection of the biological pathways in which they are enriched. LIPEA is a new valid resource for biologists and physicians to mine pathways significantly associated to a set of lipids, helping them to discover whether common and collective mechanisms are hidden behind those lipids. LIPEA was extensively tested and we provide two examples where our system gave successfully results related with Major Depression Disease (MDD) and insulin re-sistance.\n\nAvailabilityThe tool is available as web platform at https://lipea.biotec.tu-dresden.de.

bioinformatics