Factors affecting the effectiveness of biomedical document indexing and retrieval based on terminologies

International audienceThe aim of this work is to evaluate a set of indexing and retrieval strategies based on the integration of several biomedical terminologies on the available TREC Genomics collections for an ad hoc information retrieval (IR) task.Materials and methodsWe propose a multi-terminology based concept extraction approach to selecting best concepts from free text by means of voting techniques. We instantiate this general approach on four terminologies (MeSH, SNOMED, ICD-10 and GO). We particularly focus on the effect of integrating terminologies into a biomedical IR process, and the utility of using voting techniques for combining the extracted concepts from each document in order to provide a list of unique concepts.ResultsExperimental studies conducted on the TREC Genomics collections show that our multi-terminology IR approach based on voting techniques are statistically significant compared to the baseline. For example, tested on the 2005 TREC Genomics collection, our multi-terminology based IR approach provides an improvement rate of +6.98% in terms of MAP (mean average precision) (p < 0.05) compared to the baseline. In addition, our experimental results show that document expansion using preferred terms in combination with query expansion using terms from top ranked expanded documents improve the biomedical IR effectiveness.ConclusionWe have evaluated several voting models for combining concepts issued from multiple terminologies. Through this study, we presented many factors affecting the effectiveness of biomedical IR system including term weighting, query expansion, and document expansion models. The appropriate combination of those factors could be useful to improve the IR performance

Comparing Attributional and Relational Similarity as a Means to Identify Clinically Relevant Drug-gene Relationships

In emerging domains, such as precision oncology, knowledge extracted from explicit assertions may be insufficient to identify relationships of interest. One solution to this problem involves drawing inference on the basis of similarity. Computational methods have been developed to estimate the semantic similarity and relatedness between terms and relationships that are distributed across corpora of literature such as Medline abstracts and other forms of human readable text. Most research on distributional similarity has focused on the notion of attributional similarity, which estimates the similarity between entities based on the contexts in which they occur across a large corpus. A relatively under-researched area concerns relational similarity, in which the similarity between pairs of entities is estimated from the contexts in which these entity pairs occur together. While it seems intuitive that models capturing the structure of the relationships between entities might mediate the identification of biologically important relationships, there is to date no comparison of the relative utility of attributional and relational models for this purpose. In this research, I compare the performance of a range of relational and attributional similarity methods, on the task of identifying drugs that may be therapeutically useful in the context of particular aberrant genes, as identified by a team of human experts. My hypothesis is that relational similarity will be of greater utility than attributional similarity as a means to identify biological relationships that may provide answers to clinical questions, (such as “which drugs INHIBIT gene x”?) in the context of rapidly evolving domains. My results show that models based on relational similarity outperformed models based on attributional similarity on this task. As the methods explained in this research can be applied to identify any sort of relationship for which cue pairs exist, my results suggest that relational similarity may be a suitable approach to apply to other biomedical problems. Furthermore, I found models based on neural word embeddings (NWE) to be particularly useful for this task, given their higher performance than Random Indexing-based models, and significantly less computational effort needed to create them. NWE methods (such as those produced by the popular word2vec tool) are a relatively recent development in the domain of distributional semantics, and are considered by many as the state-of-the-art when it comes to semantic language modeling. However, their application in identifying biologically important relationships from Medline in general, and specifically, in the domain of precision oncology has not been well studied. The results of this research can guide the design and implementation of biomedical question answering and other relationship extraction applications for precision medicine, precision oncology and other similar domains, where there is rapid emergence of novel knowledge. The methods developed and evaluated in this project can help NLP applications provide more accurate results by leveraging corpus based methods that are by design scalable and robust

Discovering lesser known molecular players and mechanistic patterns in Alzheimer's disease using an integrative disease modelling approach

Convergence of exponentially advancing technologies is driving medical research with life changing discoveries. On the contrary, repeated failures of high-profile drugs to battle Alzheimer's disease (AD) has made it one of the least successful therapeutic area. This failure pattern has provoked researchers to grapple with their beliefs about Alzheimer's aetiology. Thus, growing realisation that Amyloid-β and tau are not 'the' but rather 'one of the' factors necessitates the reassessment of pre-existing data to add new perspectives. To enable a holistic view of the disease, integrative modelling approaches are emerging as a powerful technique. Combining data at different scales and modes could considerably increase the predictive power of the integrative model by filling biological knowledge gaps. However, the reliability of the derived hypotheses largely depends on the completeness, quality, consistency, and context-specificity of the data. Thus, there is a need for agile methods and approaches that efficiently interrogate and utilise existing public data. This thesis presents the development of novel approaches and methods that address intrinsic issues of data integration and analysis in AD research. It aims to prioritise lesser-known AD candidates using highly curated and precise knowledge derived from integrated data. Here much of the emphasis is put on quality, reliability, and context-specificity. This thesis work showcases the benefit of integrating well-curated and disease-specific heterogeneous data in a semantic web-based framework for mining actionable knowledge. Furthermore, it introduces to the challenges encountered while harvesting information from literature and transcriptomic resources. State-of-the-art text-mining methodology is developed to extract miRNAs and its regulatory role in diseases and genes from the biomedical literature. To enable meta-analysis of biologically related transcriptomic data, a highly-curated metadata database has been developed, which explicates annotations specific to human and animal models. Finally, to corroborate common mechanistic patterns — embedded with novel candidates — across large-scale AD transcriptomic data, a new approach to generate gene regulatory networks has been developed. The work presented here has demonstrated its capability in identifying testable mechanistic hypotheses containing previously unknown or emerging knowledge from public data in two major publicly funded projects for Alzheimer's, Parkinson's and Epilepsy diseases

Combining global and local semantic contexts for improving biomedical information retrieval

Présenté lors de l'European Conference on Information Retrieval 2011International audienceIn the context of biomedical information retrieval (IR), this paper explores the relationship between the document's global context and the query's local context in an attempt to overcome the term mismatch problem between the user query and documents in the collection. Most solutions to this problem have been focused on expanding the query by discovering its context, either \textit{global} or \textit{local}. In a global strategy, all documents in the collection are used to examine word occurrences and relationships in the corpus as a whole, and use this information to expand the original query. In a local strategy, the top-ranked documents retrieved for a given query are examined to determine terms for query expansion. We propose to combine the document's global context and the query's local context in an attempt to increase the term overlap between the user query and documents in the collection via document expansion (DE) and query expansion (QE). The DE technique is based on a statistical method (IR-based) to extract the most appropriate concepts (global context) from each document. The QE technique is based on a blind feedback approach using the top-ranked documents (local context) obtained in the first retrieval stage. A comparative experiment on the TREC 2004 Genomics collection demonstrates that the combination of the document's global context and the query's local context shows a significant improvement over the baseline. The MAP is significantly raised from 0.4097 to 0.4532 with a significant improvement rate of +10.62\% over the baseline. The IR performance of the combined method in terms of MAP is also superior to official runs participated in TREC 2004 Genomics and is comparable to the performance of the best run (0.4075)

Is searching full text more effective than searching abstracts?

<p>Abstract</p> <p>Background</p> <p>With the growing availability of full-text articles online, scientists and other consumers of the life sciences literature now have the ability to go beyond searching bibliographic records (title, abstract, metadata) to directly access full-text content. Motivated by this emerging trend, I posed the following question: is searching full text more effective than searching abstracts? This question is answered by comparing text retrieval algorithms on MEDLINE^®abstracts, full-text articles, and spans (paragraphs) within full-text articles using data from the TREC 2007 genomics track evaluation. Two retrieval models are examined: <it>bm25 </it>and the ranking algorithm implemented in the open-source Lucene search engine.</p> <p>Results</p> <p>Experiments show that treating an entire article as an indexing unit does not consistently yield higher effectiveness compared to abstract-only search. However, retrieval based on spans, or paragraphs-sized segments of full-text articles, consistently outperforms abstract-only search. Results suggest that highest overall effectiveness may be achieved by combining evidence from spans and full articles.</p> <p>Conclusion</p> <p>Users searching full text are more likely to find relevant articles than searching only abstracts. This finding affirms the value of full text collections for text retrieval and provides a starting point for future work in exploring algorithms that take advantage of rapidly-growing digital archives. Experimental results also highlight the need to develop distributed text retrieval algorithms, since full-text articles are significantly longer than abstracts and may require the computational resources of multiple machines in a cluster. The MapReduce programming model provides a convenient framework for organizing such computations.</p