Literature-based discovery to differentiate spectral neuropathology
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Wei, Zihan
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Abstract
Artificial intelligence-enabled literature-based discovery (LBD) provides an advanced method for performing large scale, cross-domain analyses of disease. Unlike traditional systematic reviews, which use a narrow and siloed set of data sources, LBD integrates millions of text relationships from cross-domain data sources for a more comrehensive and objective assessment. Specifically, SemNet 2.0 is a LBD software that utilizes a knowledge graph of text relationships extracted from 33+ million PubMed studies and an unsupervised learning rank aggregation algorithm to rank the importance of user-defined targets (i.e. target nodes) to related biomedical concepts (i.e. source nodes). SemNet 2.0 had previously successfully been utilized for drug repurposing, mechanism identification, and adverse event identification. In the present study, artificial intelligence-enabled LBD with SemNet 2.0 and the companion visualizer, CompositeView, was innovatively used to differentiate spectral neuropathology. Spectral neuropathology includes multi-factorial diseases where symptoms and/or underlying pathological mechanism or dynamics have overlapping similarities. SemNet 2.0 was used to specifically compare and contrast the spectral neuropathology of Alzheimer's Disease (AD), Frontotemporal Dementia (FTD), and Amyotrophic Lateral Sclerosis (ALS). Comparisons were performed to identify overlapping and differentiating Unified Medical Langauge System (UMLS) node types of amino acids, peptides and proteins (AAPP); diseases or syndromes (DSYN); and pharmacological substances (PHSU). Using percentile-ranked normalized HeteSim relevance scores and composite scores, the top 1\% of relevant nodes are reported for AD, ALS, and FTD, as well as the intersections and unions between these spectral diseases. Top-ranking nodes were mapped to functional biological processes to more holistically assess differences in underlying spectral etiology. Finally, human-in-the-loop validation was used to apply relevant context to the top-ranked nodes and functional biological processes. In conclusion, LBD results illustrate that AD, ALS and FTD share a large degree of underlying pathology dynamics, and thus, likely comprise a multi-factorial neuropathological spectrum. Small differences in the network, through either the identified genetics, co-morbidities, or environmental differences, likely shape the underlying expressed disease phenotype. Finally, the results of the present LBD study provide prioritized, testable hypotheses for future clinical or experimental research to better understand, diagnose, and treat the overlapping, spectral neuropathology of AD, FTD, and ALS.
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2024-04-29
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