The construction and publication of predications form scientific literature databases like MEDLINE is necessary due to the large amount of resources available. The main goal is to infer meaningful predicates between relevant co-occurring MeSH concepts manually annotated from MEDLINE records. The resulting predications are formed as subject-predicate-object triples. We exploit the content of MRCOC file to extract the MeSH indexing terms (main headings and subheadings) of MEDLINE. The predications were inferred by combining the semantic predicates from SemMedDB, the clustering of MeSH terms by their associated MeSH subheadings and the frequency of relevant terms in the abstracts of MEDLINE records. The inferring process also obtains and associates a weight to each generated predication. As a result, we published the generated dataset of predications using the Linked Data principles to make it available for future projects.