Analyzing LBD Methods using a General Framework

Sehgal, Aditya Kumar; Qiu, X.; Srinivasan, Padmini

doi:10.1007/978-3-540-68690-3_6

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…MetaMap) or assigned to it by human annotators [53], co-occur [54,55]. This has been referred to as the co-occurrence model [56]. These cooccurrence statistics are interpreted by correlation mining and ranking algorithms [55,57].…”

Section: Introductionmentioning

confidence: 99%

Identifying plausible adverse drug reactions using knowledge extracted from the literature

Shang

Rindflesch

et al. 2014

Journal of Biomedical Informatics

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Pharmacovigilance involves continually monitoring drug safety after drugs are put to market. To aid this process; algorithms for the identification of strongly correlated drug/adverse drug reaction (ADR) pairs from data sources such as adverse event reporting systems or Electronic Health Records have been developed. These methods are generally statistical in nature, and do not draw upon the large volumes of knowledge embedded in the biomedical literature. In this paper, we investigate the ability of scalable Literature Based Discovery (LBD) methods to identify side effects of pharmaceutical agents. The advantage of LBD methods is that they can provide evidence from the literature to support the plausibility of a drug/ ADR association, thereby assisting human review to validate the signal, which is an essential component of pharmacovigilance. To do so, we draw upon vast repositories of knowledge that has been extracted from the biomedical literature by two Natural Language Processing tools, MetaMap and SemRep. We evaluate two LBD methods that scale comfortably to the volume of knowledge available in these repositories. Specifically, we evaluate Reflective Random Indexing (RRI), a model based on concept-level co-occurrence, and Predication-based Semantic Indexing (PSI), a model that encodes the nature of the relationship between concepts to support reasoning analogically about drug-effect relationships. An evaluation set was constructed from the Side Effect Resource 2 (SIDER2), which contains known drug/ADR relations, and models were evaluated for their ability to “rediscover” these relations. In this paper, we demonstrate that both RRI and PSI can recover known drug-adverse event associations. However, PSI performed better overall, and has the additional advantage of being able to recover the literature underlying the reasoning pathways it used to make its predictions.

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Section: Introductionmentioning

confidence: 99%

Identifying plausible adverse drug reactions using knowledge extracted from the literature

Shang

Rindflesch

et al. 2014

Journal of Biomedical Informatics

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“…Swanson identified bridging concepts such as blood viscosity that could be used to connect Raynaud’s to concepts that had not occurred with it in the literature previously. This approach has been generalized and applied to a number of other problems (for recent reviews see [9–11]). The general idea is to use a bridging, or B concept, to link two other concepts, usually referred to as A and C , that have not co-occurred in the literature previously.…”

Section: Introductionmentioning

confidence: 99%

Discovering discovery patterns with predication-based Semantic Indexing

Cohen

Widdows

Schvaneveldt

et al. 2012

Journal of Biomedical Informatics

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In this paper we utilize methods of hyperdimensional computing to mediate the identification of therapeutically useful connections for the purpose of literature-based discovery. Our approach, named Predication-based Semantic Indexing, is utilized to identify empirically sequences of relationships known as “discovery patterns”, such as “drug x INHIBITS substance y, substance y CAUSES disease z” that link pharmaceutical substances to diseases they are known to treat. These sequences are derived from semantic predications extracted from the biomedical literature by the SemRep system, and subsequently utilized to direct the search for known treatments for a held out set of diseases. Rapid and efficient inference is accomplished through the application of geometric operators in PSI space, allowing for both the derivation of discovery patterns from a large set of known TREATS relationships, and the application of these discovered patterns to constrain search for therapeutic relationships at scale. Our results include the rediscovery of discovery patterns that have been constructed manually by other authors in previous research, as well as the discovery of a set of previously unrecognized patterns. The application of these patterns to direct search through PSI space results in better recovery of therapeutic relationships than is accomplished with models based on distributional statistics alone. These results demonstrate the utility of efficient approximate inference in geometric space as a means to identify therapeutic relationships, suggesting a role of these methods in drug repurposing efforts. In addition, the results provide strong support for the utility of the discovery pattern approach pioneered by Hristovski and his colleagues.

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“…As articulated by Don Swanson (1986a, 1986b, 1988), identifying such assertions is a first step in formulating and assessing new scientific hypotheses that may be regarded as potential new discoveries. Strategies for LBD have been studied primarily by information and computer scientists (see the comprehensive book edited by Bruza & Weeber, 2008, for reviews; e.g., Hristovski, Friedman, Rindflesch, & Peterlin, 2008; Sehgal, Qiu, & Srinivasan, 2008; Smalheiser & Torvik, 2008; Wren, 2008; Yetisgen‐Yildiz & Pratt, 2008). The bioinformatics community has also created numerous specialized systems that utilize implicit textual assertions for predicting, e.g., gene associations with disease and protein–protein interactions (e.g., Jansen et al, 2003; Rzhetsky, Wajngurt, Park, & Zheng, 2007; Leach et al, 2009; van Haagen et al, 2009; Tjioe, Berry, & Homayouni, 2010).…”

Section: Introductionmentioning

confidence: 99%

Literature‐based discovery: Beyond the ABCs

Smalheiser

2011

J. Am. Soc. Inf. Sci.

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Literature-based discovery (LBD) refers to a particular type of text mining that seeks to identify nontrivial assertions that are implicit, and not explicitly stated, and that are detected by juxtaposing (generally a large body of) documents. In this review, I will provide a brief overview of LBD, both past and present, and will propose some new directions for the next decade. The prevalent ABC model is not "wrong"; however, it is only one of several different types of models that can contribute to the development of the next generation of LBD tools. Perhaps the most urgent need is to develop a series of objective literaturebased interestingness measures, which can customize the output of LBD systems for different types of scientific investigations.

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Analyzing LBD Methods using a General Framework

Cited by 4 publications

References 21 publications

Identifying plausible adverse drug reactions using knowledge extracted from the literature

Identifying plausible adverse drug reactions using knowledge extracted from the literature

Discovering discovery patterns with predication-based Semantic Indexing

Literature‐based discovery: Beyond the ABCs

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