Linking hypothetical knowledge patterns to disease molecular signatures for biomarker discovery in Alzheimer’s disease

摘要

Background A number of compelling candidate Alzheimer’s biomarkers remain buried within the literature. Indeed, there should be a systematic effort towards gathering this information through approaches that mine publicly available data and substantiate supporting evidence through disease modeling methods. In the presented work, we demonstrate that an integrative gray zone mining approach can be used as a way to tackle this challenge successfully. Methods The methodology presented in this work combines semantic information retrieval and experimental data through context-specific modeling of molecular interactions underlying stages in Alzheimer’s disease (AD). Information about putative, highly speculative AD biomarkers was harvested from the literature using a semantic framework and was put into a functional context through disease- and stage-specific models. Staging models of AD were further validated for their functional relevance and novel biomarker candidates were predicted at the mechanistic level. Results Three interaction models were built representing three stages of AD, namely mild, moderate, and severe stages. Integrated analysis of these models using various arrays of evidence gathered from experimental data and published knowledge resources led to identification of four candidate biomarkers in the mild stage. Mode of action of these candidates was further reasoned in the mechanistic context of models by chains of arguments. Accordingly, we propose that some of these ‘emerging’ potential biomarker candidates have a reasonable mechanistic explanation and deserve to be investigated in more detail. Conclusions Systematic exploration of derived hypothetical knowledge leads to generation of a coherent overview on emerging knowledge niches. Integrative analysis of this knowledge in the context of disease mechanism is a promising approach towards identification of candidate biomarkers taking into consideration the complex etiology of disease. The added value of this strategy becomes apparent particularly in the area of biomarker discovery for neurodegenerative diseases where predictive biomarkers are desperately needed.