Discovering discovery patterns with predication-based Semantic Indexing

Cohen, Trevor; Widdows, Dominic; Schvaneveldt, Roger W.; Davies, P.H. O'Connor; Rindflesch, Thomas C.

doi:10.1016/j.jbi.2012.07.003

Cited by 61 publications

(62 citation statements)

References 47 publications

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“…Collective Cues: This is an approach we have pursued in our recent work [29], in which dual-predicate pathways are inferred from a set of 48,204 known TREATS relationships between diseases or syndromes (UMLS semantic type "dsyn") and pharmaceutical substances (UMLS semantic type "phsu"). For each pair, the dual-predicate path connecting the concepts concerned is inferred by generating the composite cue vector S(dysn) S(phsu) and searching through the set of vectors generated by pairwise combination of the vectors representing individual predicate paths, E(PRED1) E(PRED2).…”

Section: Discussionmentioning

confidence: 99%

“…Most paths are readily interpretable, as the ASSOCIATED WITH predicate links diseases to related biological entities, and a drug that interacts with such entities may be a plausible therapy. Some pathways are more difficult to interpret, and we refer the interested reader to a related publication [29] concerned primarily with identification, interpretation and application of such pathways. Of interest for our present purposes, directionality of the predicate paths is encoded in the complex case only.…”

Section: Discussionmentioning

confidence: 99%

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Many Paths Lead to Discovery: Analogical Retrieval of Cancer Therapies

Cohen

Widdows

Vine

et al. 2012

Quantum Interaction

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Abstract. This paper addresses the issue of analogical inference, and its potential role as the mediator of new therapeutic discoveries, by using disjunction operators based on quantum connectives to combine many potential reasoning pathways into a single search expression. In it, we extend our previous work in which we developed an approach to analogical retrieval using the Predication-based Semantic Indexing (PSI) model, which encodes both concepts and the relationships between them in high-dimensional vector space. As in our previous work, we leverage the ability of PSI to infer predicate pathways connecting two example concepts, in this case comprising of known therapeutic relationships. For example, given that drug x TREATS disease z, we might infer the predicate pathway drug x INTERACTS WITH gene y ASSOCIATED WITH disease z, and use this pathway to search for drugs related to another disease in similar ways. As biological systems tend to be characterized by networks of relationships, we evaluate the ability of quantum-inspired operators to mediate inference and retrieval across multiple relations, by testing the ability of different approaches to recover known therapeutic relationships. In addition, we introduce a novel complex vector based implementation of PSI, based on Plate's Circular Holographic Reduced Representations, which we utilize for all experiments in addition to the binary vector based approach we have applied in our previous research.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Many Paths Lead to Discovery: Analogical Retrieval of Cancer Therapies

Cohen

Widdows

Vine

et al. 2012

Quantum Interaction

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“…Side Effect Resource 2 (SIDER2) was used as data set for drug/ADR associations. The Semantic Vectors package was used to build concept-based (RRI) and predication-based (PSI) semantic space models [74,77]. …”

Section: Methodsmentioning

confidence: 99%

“…To implement a co-occurrence based approach, we use Reflective Random Indexing (RRI) [74]. To implement a discovery pattern based approach, we use Predication-based Semantic Indexing (PSI) [77]. On account of their scalability, these models permit inference on a scale that would be prohibitively time-consuming if explicit exploration of all possible reasoning pathways were attempted.…”

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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“…Recent approaches leverage distributional semantics to automatically infer discovery patterns. Specifically, the predication-based semantic indexing [9, 10] approach identifies specific patterns by encoding entities and relations linking them in a vector space using the random indexing approach [8]. This approach typically outputs a ranked list of patterns the top few of which are used to retrieve potential new treatments for target diseases.…”

Section: Background: Knowledge Acquisition and Approachesmentioning

confidence: 99%

Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations

Bakal

Talari

Kakani

et al. 2018

Journal of Biomedical Informatics

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Background Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying different causal relations between biomedical entities is also critical to understand biomedical processes. Generally, natural language processing (NLP) and machine learning are used to predict specific relations between any given pair of entities using the distant supervision approach. Objective To build high accuracy supervised predictive models to predict previously unknown treatment and causative relations between biomedical entities based only on semantic graph pattern features extracted from biomedical knowledge graphs. Methods We used 7,000 treats and 2,918 causes hand-curated relations from the UMLS Metathesaurus to train and test our models. Our graph pattern features are extracted from simple paths connecting biomedical entities in the SemMedDB graph (based on the well-known SemMedDB database made available by the U.S. National Library of Medicine). Using these graph patterns connecting biomedical entities as features of logistic regression and decision tree models, we computed mean performance measures (precision, recall, F-score) over 100 distinct 80%–20% train-test splits of the datasets. For all experiments, we used a positive:negative class imbalance of 1:10 in the test set to model relatively more realistic scenarios. Results Our models predict treats and causes relations with high F-scores of 99% and 90% respectively. Logistic regression model coefficients also help us identify highly discriminative patterns that have an intuitive interpretation. We are also able to predict some new plausible relations based on false positives that our models scored highly based on our collaborations with two physician co-authors. Finally, our decision tree models are able to retrieve over 50% of treatment relations from a recently created external dataset. Conclusions We employed semantic graph patterns connecting pairs of candidate entities in a knowledge graph as features to predict treatment/causative relations between them. We provide what we believe is the first evidence in direct prediction of biomedical relations based on graph features. Our work complements lexical pattern based approaches in that the graph patterns can be used as additional features for weakly supervised relation prediction.

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Discovering discovery patterns with predication-based Semantic Indexing

Cited by 61 publications

References 47 publications

Many Paths Lead to Discovery: Analogical Retrieval of Cancer Therapies

Many Paths Lead to Discovery: Analogical Retrieval of Cancer Therapies

Identifying plausible adverse drug reactions using knowledge extracted from the literature

Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations

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