The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
Ramón Alonso Allende - One of the best experts on this subject based on the ideXlab platform.
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Accelerating searches of research grants and Scientific Literature with novo|[verbar]|seekSM
Nature Methods, 2009Co-Authors: Ramón Alonso AllendeAbstract:Bioalma's novo|seek is a powerful alternative to traditional Literature searching on the web. Designed to help scientists with their research projects and proposals within the life science community, novo|seek is a dynamic information extraction system for quickly identifying key concepts found in published papers. Here we demonstrate how a single concept can initiate complete exploration of Scientific Literature, current research grants and external web-based resources.
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Accelerating searches of research grants and Scientific Literature with novo|seek^SM
Nature Methods, 2009Co-Authors: Ramón Alonso AllendeAbstract:Bioalma's novo|seek is a powerful alternative to traditional Literature searching on the web. Designed to help scientists with their research projects and proposals within the life science community, novo|seek is a dynamic information extraction system for quickly identifying key concepts found in published papers. Here we demonstrate how a single concept can initiate complete exploration of Scientific Literature, current research grants and external web-based resources.
Ourania Konstanti - One of the best experts on this subject based on the ideXlab platform.
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AIME - Relation mining over a corpus of Scientific Literature
Artificial Intelligence in Medicine, 2005Co-Authors: Fabio Rinaldi, Gerold Schneider, Kaarel Kaljurand, Michael Hess, Christos Andronis, Andreas Persidis, Ourania KonstantiAbstract:The amount of new discoveries (as published in the Scientific Literature) in the area of Molecular Biology is currently growing at an exponential rate. This growth makes it very difficult to filter the most relevant results, and the extraction of the core information, for inclusion in one of the knowledge resources being maintained by the research community, becomes very expensive. Therefore, there is a growing interest in text processing approaches that can deliver selected information from Scientific publications, which can limit the amount of human intervention normally needed to gather those results. This paper presents and evaluates an approach aimed at automating the process of extracting semantic relations (e.g. interactions between genes and proteins) from Scientific Literature in the domain of Molecular Biology. The approach, using a novel dependency-based parser, is based on a complete syntactic analysis of the corpus.
Daniel S Weld - One of the best experts on this subject based on the ideXlab platform.
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high precision extraction of emerging concepts from Scientific Literature
International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020Co-Authors: Daniel King, Doug Downey, Daniel S WeldAbstract:Identification of new concepts in Scientific Literature can help power faceted search, Scientific trend analysis, knowledge-base construction, and more, but current methods are lacking. Manual identification can't keep up with the torrent of new publications, while the precision of existing automatic techniques is too low for many applications. We present an unsupervised concept extraction method for Scientific Literature that achieves much higher precision than previous work. Our approach relies on a simple but novel intuition: each Scientific concept is likely to be introduced or popularized by a single paper that is disproportionately cited by subsequent papers mentioning the concept. From a corpus of computer science papers on arXiv, we find that our method achieves a Precision@1000 of 99%, compared to 86% for prior work, and a substantially better precision-yield trade-off across the top 15,000 extractions. To stimulate research in this area, we release our code and data.
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SIGIR - High-Precision Extraction of Emerging Concepts from Scientific Literature
Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020Co-Authors: Daniel King, Doug Downey, Daniel S WeldAbstract:Identification of new concepts in Scientific Literature can help power faceted search, Scientific trend analysis, knowledge-base construction, and more, but current methods are lacking. Manual identification can't keep up with the torrent of new publications, while the precision of existing automatic techniques is too low for many applications. We present an unsupervised concept extraction method for Scientific Literature that achieves much higher precision than previous work. Our approach relies on a simple but novel intuition: each Scientific concept is likely to be introduced or popularized by a single paper that is disproportionately cited by subsequent papers mentioning the concept. From a corpus of computer science papers on arXiv, we find that our method achieves a Precision@1000 of 99%, compared to 86% for prior work, and a substantially better precision-yield trade-off across the top 15,000 extractions. To stimulate research in this area, we release our code and data.
Melissa Mccartney - One of the best experts on this subject based on the ideXlab platform.
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Annotated primary Scientific Literature: A pedagogical tool for undergraduate courses.
PLoS biology, 2019Co-Authors: Matthew Kararo, Melissa MccartneyAbstract:Annotated primary Scientific Literature is a teaching and learning resource that provides scaffolding for undergraduate students acculturating to the authentic Scientific practice of obtaining and evaluating information through the medium of primary Scientific Literature. Utilizing annotated primary Scientific Literature as an integrated pedagogical tool could enable more widespread use of primary Scientific Literature in undergraduate science classrooms with minimal disruption to existing syllabi. Research is ongoing to determine an optimal implementation protocol, with these preliminary iterations presented here serving as a first look at how students respond to annotated primary Scientific Literature. The undergraduate biology student participants in our study did not, in general, have an abundance of experience reading primary Scientific Literature; however, they found the annotations useful, especially for vocabulary and graph interpretation. We present here an implementation protocol for using annotated primary Literature in the classroom that minimizes the use of valuable classroom time and requires no additional pedagogical training for instructors.
Miguel A. Andrade - One of the best experts on this subject based on the ideXlab platform.
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Update on XplorMed: a web server for exploring Scientific Literature
Nucleic acids research, 2003Co-Authors: Carolina Perez-iratxeta, Antonio Jesús Pérez, Peer Bork, Miguel A. AndradeAbstract:As Scientific Literature databases like MEDLINE increase in size, so does the time required to search them. Scientists must frequently inspect long lists of references manually, often just reading the titles. XplorMed is a web tool that aids MEDLINE searching by summarizing the subjects contained in the results, thus allowing users to focus on subjects of interest. Here we describe new features added to XplorMed during the last 2 years (http://www.bork.embl-heidelberg.de/xplormed/).
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A protocol for the update of references to Scientific Literature in biological databases.
Applied bioinformatics, 2003Co-Authors: Carolina Perez-iratxeta, Peer Bork, Nagore Astola, Francesca D. Ciccarelli, Parantu K. Sha, Miguel A. AndradeAbstract:Entries in biological databases are usually linked to Scientific references. To generate those links and to keep them up-to-date, database maintainers have to continuously scan the Scientific Literature to select references that are relevant for each single database entry. The continuous growth of both the corpus of Scientific Literature and the size of biological databases makes this task very hard. We present a protocol intended to assist the updating of an existing set of Literature (abstract) links from a single database entry with new references. It consists of taking the set of MEDLINE neighbour references of the existing linked abstracts and evaluating their relevance according to the existing set of abstracts. To test the applicability of the algorithm, we did a simple benchmark of the system using the references associated with the entries of a protein domain database. Human experts found the references that the algorithm scored highly were more relevant to the database entry than those scored lowly, suggesting that the algorithm was useful.
