Phenotypic similarity between diseases is an important factor in biomedical research since similar diseases often share similar molecular origins. This forms the basis of an inference-led approach to disease characterisation known as Phenotype Triangulation.
However, evidence of disease similarity is often hidden within unstructured biomedical literature and often not presented as direct evidence, necessitating a time consuming and costly review process to identify relevant linkages. Such linkages are particularly challenging to find for rare diseases for which the amount of existing research to draw from is still at a relatively low volume.
SciBite has developed a method that combines Semantic Analytics and Machine Learning to unlock the potential of biomedical literature and successfully predict disease relationships without any prior knowledge of the diseases, based on the strength of indirect evidence.
To learn more, download the full use case.
In celebration of Rare Disease Day 28th Feb, we have a 3 part blog post looking into some of the challenges/analysis techniques involved in the research process.Read
Today, we’ll look at a fresh way of enabling scientific researchers, either in pharmaceutical R&D or in medical institutes to deepen their investigations and consider new links.Read
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