Nicola Bernabò

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The rapid growth of published literature makes biomedical text mining increasingly invaluable for unpacking implicit knowledge hidden in unstructured text. We employed biomedical text mining and biological networks analyses to research the process of sperm egg recognition and binding (SERB). We selected from the literature the molecules expressed either on(More)
In this paper we represented Spermatozoa Activation (SA) the process that leads male gametes to reach their fertilising ability of sea urchin, Caenorhabditis elegans and human as biological networks, i.e. as networks of nodes (molecules) linked by edges (their interactions). Then, we compared them with networks representing ten pathways of relevant(More)
We represented the endocannabinoid system (ECS) as a biological network, where ECS molecules are the nodes (123) and their interactions the links (189). ECS network follows a scale-free topology, which confers robustness against random damage, easy navigability, and controllability. Network topological parameters, such as clustering coefficient (i.e., how(More)
Here we realized a networks-based model representing the process of actin remodelling that occurs during the acquisition of fertilizing ability of human spermatozoa (HumanMade_ActinSpermNetwork, HM_ASN). Then, we compared it with the networks provided by two different text mining tools: Agilent Literature Search (ALS) and PESCADOR. As a reference, we used(More)
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