While unsupervised machine learning has been advanced to study the semantic relationships between word embeddings (Mikolov et al., 2013a; LeCun et al., 2015) and applied to the material science corpus (Tshitoyan et al., 2019), this has not been scaled-up to extract the ‘global context’ of conceptual associations from the entirety of publicly available unstructured biomedical text. Additionally, a principled way of accounting for the distances between phrases captured from the ever-growing scientific literature has not been comprehensively researched to quantify the strength of ‘local context’ between pairs of biological concepts. Given the propensity for irreproducible or erroneous scientific research (Nature Editorial, 2016), any local or global signals extracted from this unstructured knowledge need to be seamlessly triangulated with deep biological insights emergent from various omics data silos.