Semi-automated literature mining to identify putative biomarkers of disease from multiple biofluids

Jordan, Rick; Visweswaran, Shyam; Gopalakrishnan, Vanathi

doi:10.1186/2043-9113-4-13

Cited by 6 publications

(3 citation statements)

References 34 publications

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“…The ELISA approaches applied here were successful in reliably quantifying concentrations of the selected proteins in saliva ranging from picogram/ml to µicrogram/ml with the inter- and intra-assay CVs being < 10%, in line with earlier recommendations 25 . In order to evaluate and prioritize candidate biomarkers, we used a simple approach by combining accumulated evidence and our previously published studies rather than automated literature mining methods 26 .…”

Section: Discussionmentioning

confidence: 99%

Validation and verification of predictive salivary biomarkers for oral health

Bostancı

Mitsakakis

Afacan

et al. 2021

Sci Rep

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Oral health is important not only due to the diseases emerging in the oral cavity but also due to the direct relation to systemic health. Thus, early and accurate characterization of the oral health status is of utmost importance. There are several salivary biomarkers as candidates for gingivitis and periodontitis, which are major oral health threats, affecting the gums. These need to be verified and validated for their potential use as differentiators of health, gingivitis and periodontitis status, before they are translated to chair-side for diagnostics and personalized monitoring. We aimed to measure 10 candidates using high sensitivity ELISAs in a well-controlled cohort of 127 individuals from three groups: periodontitis (60), gingivitis (31) and healthy (36). The statistical approaches included univariate statistical tests, receiver operating characteristic curves (ROC) with the corresponding Area Under the Curve (AUC) and Classification and Regression Tree (CART) analysis. The main outcomes were that the combination of multiple biomarker assays, rather than the use of single ones, can offer a predictive accuracy of > 90% for gingivitis versus health groups; and 100% for periodontitis versus health and periodontitis versus gingivitis groups. Furthermore, ratios of biomarkers MMP-8, MMP-9 and TIMP-1 were also proven to be powerful differentiating values compared to the single biomarkers.

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Section: Discussionmentioning

confidence: 99%

Validation and verification of predictive salivary biomarkers for oral health

Bostancı

Mitsakakis

Afacan

et al. 2021

Sci Rep

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“…Especially, text mining techniques such as NER, event extraction, or dependency parser enable mining more plentiful information and knowledge with a wider diversity from the academic literature, whose amount is too heavy to be manually handled and digested. Investigators have indeed attempted to find not only the hidden relationships between different pairs of entity types like protein-disease or drug-disease [ 2 , 8 , 9 ], but also biomarkers [ 10 ], and drug indications [ 11 ]. One example is that Vos et al derived new plausible multimorbidity patterns of psychiatric and somatic diseases using automated concept recognition and profiling [ 12 ].…”

Section: Discussionmentioning

confidence: 99%

Enriching plausible new hypothesis generation in PubMed

et al. 2017

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BackgroundMost of earlier studies in the field of literature-based discovery have adopted Swanson's ABC model that links pieces of knowledge entailed in disjoint literatures. However, the issue concerning their practicability remains to be solved since most of them did not deal with the context surrounding the discovered associations and usually not accompanied with clinical confirmation. In this study, we aim to propose a method that expands and elaborates the existing hypothesis by advanced text mining techniques for capturing contexts. We extend ABC model to allow for multiple B terms with various biological types.ResultsWe were able to concretize a specific, metabolite-related hypothesis with abundant contextual information by using the proposed method. Starting from explaining the relationship between lactosylceramide and arterial stiffness, the hypothesis was extended to suggest a potential pathway consisting of lactosylceramide, nitric oxide, malondialdehyde, and arterial stiffness. The experiment by domain experts showed that it is clinically valid.ConclusionsThe proposed method is designed to provide plausible candidates of the concretized hypothesis, which are based on extracted heterogeneous entities and detailed relation information, along with a reliable ranking criterion. Statistical tests collaboratively conducted with biomedical experts provide the validity and practical usefulness of the method unlike previous studies. Applying the proposed method to other cases, it would be helpful for biologists to support the existing hypothesis and easily expect the logical process within it.

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“…Another used citations containing the PubMed MeSH term human and containing sentences related to interferon-gamma, from which relationships were extracted and ranked using graph metrics [ 46 ]. Jordan et al [ 47 ] present a keyword search method for identifying putative biomarkers for breast and lung cancer by searching for genes and proteins associated with a biological fluid keyword and either cancer. However, none of this work has made use of semantic predications, as we have, in the formation of an interaction network.…”

Section: Introductionmentioning

confidence: 99%

Networks of neuroinjury semantic predications to identify biomarkers for mild traumatic brain injury

et al. 2015

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ObjectiveMild traumatic brain injury (mTBI) has high prevalence in the military, among athletes, and in the general population worldwide (largely due to falls). Consequences can include a range of neuropsychological disorders. Unfortunately, such neural injury often goes undiagnosed due to the difficulty in identifying symptoms, so the discovery of an effective biomarker would greatly assist diagnosis; however, no single biomarker has been identified. We identify several body substances as potential components of a panel of biomarkers to support the diagnosis of mild traumatic brain injury.MethodsOur approach to diagnostic biomarker discovery combines ideas and techniques from systems medicine, natural language processing, and graph theory. We create a molecular interaction network that represents neural injury and is composed of relationships automatically extracted from the literature. We retrieve citations related to neurological injury and extract relationships (semantic predications) that contain potential biomarkers. After linking all relationships together to create a network representing neural injury, we filter the network by relationship frequency and concept connectivity to reduce the set to a manageable size of higher interest substances.Results99,437 relevant citations yielded 26,441 unique relations. 18,085 of these contained a potential biomarker as subject or object with a total of 6246 unique concepts. After filtering by graph metrics, the set was reduced to 1021 relationships with 49 unique concepts, including 17 potential biomarkers.ConclusionWe created a network of relationships containing substances derived from 99,437 citations and filtered using graph metrics to provide a set of 17 potential biomarkers. We discuss the interaction of several of these (glutamate, glucose, and lactate) as the basis for more effective diagnosis than is currently possible. This method provides an opportunity to focus the effort of wet bench research on those substances with the highest potential as biomarkers for mTBI.

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Semi-automated literature mining to identify putative biomarkers of disease from multiple biofluids

Cited by 6 publications

References 34 publications

Validation and verification of predictive salivary biomarkers for oral health

Validation and verification of predictive salivary biomarkers for oral health

Enriching plausible new hypothesis generation in PubMed

Networks of neuroinjury semantic predications to identify biomarkers for mild traumatic brain injury

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