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 spermatozoa or on oocytes thought to be involved in SERB and, using an automated literature search software (Agilent Literature Search), we realized a network, SERBN, characterized by a hierarchical scale free and a small world topology. We used an integrated approach, either based on selection of hubs or by a cluster analysis, to discern the key molecules of SERB. We found that in most cases some of them are not directly situated on spermatozoa and oocyte, but are dispersed in oviductal fluid or embedded in exosomes present in the perivitelline space. To confirm and validate our results, we performed further analyses using STRING and Reactome FI software. Our findings underscore that the fertility is not a property of gametes in isolation, but rather depends on the functional integrity of the entire reproductive system. These observations collectively underscore the importance of integrative biology in exploring biological systems and in rethinking of fertility mechanisms in the light of this innovative approach.
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