42,631 research outputs found

    On the use of clustering and the MeSH controlled vocabulary to improve MEDLINE abstract search

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    Databases of genomic documents contain substantial amounts of structured information in addition to the texts of titles and abstracts. Unstructured information retrieval techniques fail to take advantage of the structured information available. This paper describes a technique to improve upon traditional retrieval methods by clustering the retrieval result set into two distinct clusters using additional structural information. Our hypothesis is that the relevant documents are to be found in the tightest cluster of the two, as suggested by van Rijsbergen's cluster hypothesis. We present an experimental evaluation of these ideas based on the relevance judgments of the 2004 TREC workshop Genomics track, and the CLUTO software clustering package

    Experiments in terabyte searching, genomic retrieval and novelty detection for TREC 2004

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    In TREC2004, Dublin City University took part in three tracks, Terabyte (in collaboration with University College Dublin), Genomic and Novelty. In this paper we will discuss each track separately and present separate conclusions from this work. In addition, we present a general description of a text retrieval engine that we have developed in the last year to support our experiments into large scale, distributed information retrieval, which underlies all of the track experiments described in this document

    VIDA: a virus database system for the organization of animal virus genome open reading frames

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    VIDA is a new virus database that organizes open reading frames (ORFs) from partial and complete genomic sequences from animal viruses. Currently VIDA includes all sequences from GenBank for Herpesviridae, Coronaviridae and Arteriviridae. The ORFs are organized into homologous protein families, which are identified on the basis of sequence similarity relationships, Conserved sequence regions of potential functional importance are identified and can be retrieved as sequence alignments. We use a controlled taxonomical and functional classification for all the proteins and protein families in the database. When available, protein structures that are related to the families have also been included. The database is available for online search and sequence information retrieval at http://www.biochem.ucl.ac.uk/bsm/virus-database/ VIDA.html

    Structural fingerprints of transcription factor binding site regions

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    Fourier transforms are a powerful tool in the prediction of DNA sequence properties, such as the presence/absence of codons. We have previously compiled a database of the structural properties of all 32,896 unique DNA octamers. In this work we apply Fourier techniques to the analysis of the structural properties of human chromosomes 21 and 22 and also to three sets of transcription factor binding sites within these chromosomes. We find that, for a given structural property, the structural property power spectra of chromosomes 21 and 22 are strikingly similar. We find common peaks in their power spectra for both Sp1 and p53 transcription factor binding sites. We use the power spectra as a structural fingerprint and perform similarity searching in order to find transcription factor binding site regions. This approach provides a new strategy for searching the genome data for information. Although it is difficult to understand the relationship between specific functional properties and the set of structural parameters in our database, our structural fingerprints nevertheless provide a useful tool for searching for function information in sequence data. The power spectrum fingerprints provide a simple, fast method for comparing a set of functional sequences, in this case transcription factor binding site regions, with the sequences of whole chromosomes. On its own, the power spectrum fingerprint does not find all transcription factor binding sites in a chromosome, but the results presented here show that in combination with other approaches, this technique will improve the chances of identifying functional sequences hidden in genomic data

    Applying Biomedical Ontologies on Semantic Query Expansion

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    *1- Introduction*

The interpretation of a question (or information need) depends, among other things, of a series of lexicalsemantic relations that complement and help the cognitive process of answering that information need. Despite this fact, currently used information retrieval mechanisms take few advantages of the semantic interpretation of users’ information needs (usually specified through keywords). In most of the cases, those mechanisms are based on keyword matching, and thus are excessively dependant on the query and document terms.

There are several past results showing that, in general, information retrieval based on domain knowledge decreases the accuracy of keyword based search engines. We believe this approach deserves further discussion and experimentation, looking for more strong evidences that these negative results can really be generalized. Moreover, there are some questions left unanswered by previous work that our experiment is addressing:

(_i_) Using a scientific ontology, with formal construction and maintenance processes, such as the OBO ontologies, would produce better results? 

(_ii_) Are there more efficient query expansion techniques using available domain knowledge?

(_iii_) Is a scientific ontology complete enough to fulfill the information retrieval researchers’ needs, in general?

*2- Semantic Query Expansion*

To try to answer some of these questions, we run a query expansion experiment using the Gene Ontology (GO) as domain knowledge. As the document repository, we used an extraction of 10 years of PubMed publications (from 1994 to 2004), which contains approximately 4.6 Million documents. This dataset is a test collection used by the information retrieval community, called Genomic TREC.

*3- Results*
To evaluate our ontology-based semantic query expansion technique, we measured the effectiveness of the information retrieval mechanism with and without expansion. In a nutshell, the average result showed an increase of 28% on synonyms relations and a small decrease on other relations.

Our results show a lot of consistence with past related work. In fact, if the expansion strategy does not selectively choose when and how to expand, only synonym relations are worth to be used. However, looking further, it is possible to find several opportunities to try other expansion strategies. For example, the problem with query expansion using generalization/specialization relationships is that, if it is always applied, the bad results are more frequent than the good ones. But, if the strategy is to be selective on when to use these relations for expansion, the increasing on accuracy can be outstanding. As shown by our experiment, there was a query with 98% increment on effectiveness. 

*4- Conclusion*
We strongly believe that it is premature to assume that semantics-based query expansion is, in general, a recall-enhancing, precision-degrading technique. Our experiments suggest that by using scientific based ontologies (like OBO ontologies) with formal relations, it is possible to increase both recall and precision. Our group is currently revising this first experiment towards a better semantic query expansion strategy.

*5- Acknowledgements*
This work was partially funded by CAPES and CNPq research grants 311454/2006-2, 306889/2007-2 and 484713/2007-8.

*References*
_Fox E. Lexical relations enhancing effectiveness of information retrieval systems. SIGIR Forum, New York, v.15, n.3, p.5-3._

_Voorhees E. Query expansion using lexicalsemantic relations. In: ACM SIGIR conference on research and development in information retrieval, Proceedings, Dublin:17, p.61–69, 1994

    Interrogation of modern and ancient genomes reveals the complex domestic history of cattle

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    The analysis of mitochondrial and nuclear DNA sequence polymorphisms from modern cattle populations has had a profound impact on our understanding of the events surrounding the domestication of cattle. From these studies, it has been possible to distinguish between pre- and post-domestic genetic differentiation, supporting previous assertions from archaeological studies and, in some cases, revealing novel aspects of the demographic history of cattle. Analyses of genetic material retrieved from the remains of extinct ancestral wild cattle have also added valuable layers of information pertaining to cattle domestic origins; however, information from these investigations have, in general, been limited to small, variable portions of the mitochondrial genome owing to technical challenges associated with the retrieval and amplification of ancient DNA. In recent years, however, new high-throughput, massively parallel genomics technology platforms, such as single-nucleotide polymorphism (SNP) genotyping arrays and next-generation sequencing (NGS), have provided a new impetus to the studies of genetic variation in extant and ancient cattle. Arrays of SNP have facilitated high-resolution genetic surveys of global cattle populations and detection of ancient and recent genomic selective sweeps. Next-generation sequencing analyses of modern and ancient cattle hold great promise for identifying and cataloging of pre- and post-domestication patterns of genomic variation and correlating this with natural and artificial selection processes

    Systematizing Genome Privacy Research: A Privacy-Enhancing Technologies Perspective

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    Rapid advances in human genomics are enabling researchers to gain a better understanding of the role of the genome in our health and well-being, stimulating hope for more effective and cost efficient healthcare. However, this also prompts a number of security and privacy concerns stemming from the distinctive characteristics of genomic data. To address them, a new research community has emerged and produced a large number of publications and initiatives. In this paper, we rely on a structured methodology to contextualize and provide a critical analysis of the current knowledge on privacy-enhancing technologies used for testing, storing, and sharing genomic data, using a representative sample of the work published in the past decade. We identify and discuss limitations, technical challenges, and issues faced by the community, focusing in particular on those that are inherently tied to the nature of the problem and are harder for the community alone to address. Finally, we report on the importance and difficulty of the identified challenges based on an online survey of genome data privacy expertsComment: To appear in the Proceedings on Privacy Enhancing Technologies (PoPETs), Vol. 2019, Issue

    StemNet: An Evolving Service for Knowledge Networking in the Life Sciences

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    Up until now, crucial life science information resources, whether bibliographic or factual databases, are isolated from each other. Moreover, semantic metadata intended to structure their contents is supplied in a manual form only. In the StemNet project we aim at developing a framework for semantic interoperability for these resources. This will facilitate the extraction of relevant information from textual sources and the generation of semantic metadata in a fully automatic manner. In this way, (from a computational perspective) unstructured life science documents are linked to structured biological fact databases, in particular to the identifiers of genes, proteins, etc. Thus, life scientists will be able to seamlessly access information from a homogeneous platform, despite the fact that the original information was unlinked and scattered over the whole variety of heterogeneous life science information resources and, therefore, almost inaccessible for integrated systematic search by academic, clinical, or industrial users
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