An automated framework for hypotheses generation using literature

Abstract

BackgroundIn bio-medicine, exploratory studies and hypothesis generation often begin with researching existing literature to identify a set of factors and their association with diseases, phenotypes, or biological processes. Many scientists are overwhelmed by the sheer volume of literature on a disease when they plan to generate a new hypothesis or study a biological phenomenon. The situation is even worse for junior investigators who often find it difficult to formulate new hypotheses or, more importantly, corroborate if their hypothesis is consistent with existing literature. It is a daunting task to be abreast with so much being published and also remember all combinations of direct and indirect associations. Fortunately there is a growing trend of using literature mining and knowledge discovery tools in biomedical research. However, there is still a large gap between the huge amount of effort and resources invested in disease research and the little effort in harvesting the published knowledge. The proposed hypothesis generation framework (HGF) finds “crisp semantic associations” among entities of interest - that is a step towards bridging such gaps.MethodologyThe proposed HGF shares similar end goals like the SWAN but are more holistic in nature and was designed and implemented using scalable and efficient computational models of disease-disease interaction. The integration of mapping ontologies with latent semantic analysis is critical in capturing domain specific direct and indirect “crisp” associations, and making assertions about entities (such as disease X is associated with a set of factors Z).ResultsPilot studies were performed using two diseases. A comparative analysis of the computed “associations” and “assertions” with curated expert knowledge was performed to validate the results. It was observed that the HGF is able to capture “crisp” direct and indirect associations, and provide knowledge discovery on demand.ConclusionsThe proposed framework is fast, efficient, and robust in generating new hypotheses to identify factors associated with a disease. A full integrated Web service application is being developed for wide dissemination of the HGF. A large-scale study by the domain experts and associated researchers is underway to validate the associations and assertions computed by the HGF.

Obnet: Network of semantic associations for obesity

“…A Query-Based Sampling (QBS) process [ 3 ] was used to create balanced and “Representative Samples” for modeling the ObNet. A relevance model [ 1 ] was adopted to categorize associations in three groups: Clear Association, Established Concepts, and Current Research Topics.…”

“…To understand complex relationships among all contributing factors, we have developed the ObNet (Network of Semantic Association for Obesity). The concepts were adopted from ARIANA [ 1 , 2 ]. The Local Latent Semantic Analysis tool [ 3 ], developed in the CVPIA lab, was used to extract network of semantic associations.…”

Validation and Topic-driven Ranking for Biomedical Hypothesis Generation Systems

“…This ranges from comparisons between system output and expert output, such as the analysis done on the Manjal system [44], to incorporating expert opinion into goldstandard terms for LitLinker [32], to running actual experiments on potential results by Wren et al [54]. Expert opinion is at the heart of many recent systems [51,49,27,10,46,2], including the previous version of our own. This process is both time consuming and risks introducing significant bias into the validation.…”

“…The importance of these systems rises alongside the pace of scientific output; an abundance of literature implies an abundance of implicit connections. Yet, while many projects focus on new ways to find potential discoveries [51,49,27,10,46,2,42], few have a way to rigorously verify the quality of the connections posed by their system [7].…”

“…Without a viable alternative, the HG community primarily turns to expert analysis in order to validate the output of these systems [7,51,49,27,10,46,2]. Many methods also require an expert's input to rank their potential connections [49,27,46].…”