Development of a text search engine for medicinal chemistry patents

Pasche, Emilie; Gobeill, Julien; Oezdemir-Zaech, Fatma; Vachon, Therese; Lovis, Christian; Ruch, Patrick

doi:10.14806/ej.18.b.545

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…The biomedical community is increasingly developing data management systems based on advanced text analytics to cope with the extremely large size and variety of datasets involved in scientific research ( 6–10 , 39 ). Indeed, this trend is not specific to publicly available data but also directly affects large pharma and biotech companies, which are struggling to transform silo-based drug development models into more integrated platforms powered with advanced text analytics ( 40 ). Here, the dataset retrieval model is especially adequate if we consider that user queries in such large companies are likely to involve simultaneously a large set of orthogonal entities (a chemical compound, a therapeutic indication, a genetic profile, etc.)…”

Section: Discussionmentioning

confidence: 99%

Improving average ranking precision in user searches for biomedical research datasets

et al. 2017

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Availability of research datasets is keystone for health and life science study reproducibility and scientific progress. Due to the heterogeneity and complexity of these data, a main challenge to be overcome by research data management systems is to provide users with the best answers for their search queries. In the context of the 2016 bioCADDIE Dataset Retrieval Challenge, we investigate a novel ranking pipeline to improve the search of datasets used in biomedical experiments. Our system comprises a query expansion model based on word embeddings, a similarity measure algorithm that takes into consideration the relevance of the query terms, and a dataset categorization method that boosts the rank of datasets matching query constraints. The system was evaluated using a corpus with 800k datasets and 21 annotated user queries, and provided competitive results when compared to the other challenge participants. In the official run, it achieved the highest infAP, being +22.3% higher than the median infAP of the participant’s best submissions. Overall, it is ranked at top 2 if an aggregated metric using the best official measures per participant is considered. The query expansion method showed positive impact on the system’s performance increasing our baseline up to +5.0% and +3.4% for the infAP and infNDCG metrics, respectively. The similarity measure algorithm showed robust performance in different training conditions, with small performance variations compared to the Divergence from Randomness framework. Finally, the result categorization did not have significant impact on the system’s performance. We believe that our solution could be used to enhance biomedical dataset management systems. The use of data driven expansion methods, such as those based on word embeddings, could be an alternative to the complexity of biomedical terminologies. Nevertheless, due to the limited size of the assessment set, further experiments need to be performed to draw conclusive results. Database URL: https://biocaddie.org/benchmark-data

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Section: Discussionmentioning

confidence: 99%

Improving average ranking precision in user searches for biomedical research datasets

et al. 2017

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“…The finally delivered search engine has been implemented as a web application that we briefly describe at the end of the paper. This paper is an extended version of an original extended abstract submitted to NETTAB 2012 [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Development and tuning of an original search engine for patent libraries in medicinal chemistry

et al. 2014

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BackgroundThe large increase in the size of patent collections has led to the need of efficient search strategies. But the development of advanced text-mining applications dedicated to patents of the biomedical field remains rare, in particular to address the needs of the pharmaceutical & biotech industry, which intensively uses patent libraries for competitive intelligence and drug development.MethodsWe describe here the development of an advanced retrieval engine to search information in patent collections in the field of medicinal chemistry. We investigate and combine different strategies and evaluate their respective impact on the performance of the search engine applied to various search tasks, which covers the putatively most frequent search behaviours of intellectual property officers in medical chemistry: 1) a prior art search task; 2) a technical survey task; and 3) a variant of the technical survey task, sometimes called known-item search task, where a single patent is targeted.ResultsThe optimal tuning of our engine resulted in a top-precision of 6.76% for the prior art search task, 23.28% for the technical survey task and 46.02% for the variant of the technical survey task. We observed that co-citation boosting was an appropriate strategy to improve prior art search tasks, while IPC classification of queries was improving retrieval effectiveness for technical survey tasks. Surprisingly, the use of the full body of the patent was always detrimental for search effectiveness. It was also observed that normalizing biomedical entities using curated dictionaries had simply no impact on the search tasks we evaluate. The search engine was finally implemented as a web-application within Novartis Pharma. The application is briefly described in the report.ConclusionsWe have presented the development of a search engine dedicated to patent search, based on state of the art methods applied to patent corpora. We have shown that a proper tuning of the system to adapt to the various search tasks clearly increases the effectiveness of the system. We conclude that different search tasks demand different information retrieval engines' settings in order to yield optimal end-user retrieval.

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Development of a text search engine for medicinal chemistry patents

Cited by 2 publications

References 6 publications

Improving average ranking precision in user searches for biomedical research datasets

Improving average ranking precision in user searches for biomedical research datasets

Development and tuning of an original search engine for patent libraries in medicinal chemistry

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