Biomedical subjects

Christine Chichester

Publications and source records attributed to Christine Chichester.

5 recordsLinked to original sources

PubMed2006

Using argumentation to extract key sentences from biomedical abstracts.

PROBLEM: key word assignment has been largely used in MEDLINE to provide an indicative "gist" of the content of articles and to help retrieving biomedical articles. Abstracts are also used for this purpose. However with usually more than 300 words, MEDLINE abstracts can still be regarded as long documents; therefore we design a system to select a unique key sentence. This key sentence must be indicative of the article's content and we assume that abstract's conclusions are good candidates. We design and assess the performance of an automatic key sentence selector, which classifies sentences into four argumentative moves: PURPOSE, METHODS, RESULTS and CONCLUSION METHODS: we rely on Bayesian classifiers trained on automatically acquired data. Features representation, selection and weighting are reported and classification effectiveness is evaluated on the four classes using confusion matrices. We also explore the use of simple heuristics to take the position of sentences into account. Recall, precision and F-scores are computed for the CONCLUSION class. For the CONCLUSION class, the F-score reaches 84%. Automatic argumentative classification using Bayesian learners is feasible on MEDLINE abstracts and should help user navigation in such repositories.

Abstracting and Indexing↗

PubMed2005

Using argumentation to retrieve articles with similar citations: an inquiry into improving related articles search in the MEDLINE digital library.

The aim of this study is to investigate the relationships between citations and the scientific argumentation found abstracts. We design a related article search task and observe how the argumentation can affect the search results. We extracted citation lists from a set of 3200 full-text papers originating from a narrow domain. In parallel, we recovered the corresponding MEDLINE records for analysis of the argumentative moves. Our argumentative model is founded on four classes: PURPOSE, METHODS, RESULTS and CONCLUSION. A Bayesian classifier trained on explicitly structured MEDLINE abstracts generates these argumentative categories. The categories are used to generate four different argumentative indexes. A fifth index contains the complete abstract, together with the title and the list of Medical Subject Headings (MeSH) terms. To appraise the relationship of the moves to the citations, the citation lists were used as the criteria for determining relatedness of articles, establishing a benchmark; it means that two articles are considered as "related" if they share a significant set of co-citations. Our results show that the average precision of queries with the PURPOSE and CONCLUSION features is the highest, while the precision of the RESULTS and METHODS features was relatively low. A linear weighting combination of the moves is proposed, which significantly improves retrieval of related articles.

Abstracting and Indexing↗

PubMed2005

Extracting key sentences with latent argumentative structuring.

PROBLEM: Key word assignment has been largely used in MEDLINE to provide an indicative "gist" of the content of articles. Abstracts are also used for this purpose. However with usually more than 300 words, abstracts can still be regarded as long documents; therefore we design a system to select a unique key sentence. This key sentence must be indicative of the article's content and we assume that abstract's conclusions are good candidates. We design and assess the performance of an automatic key sentence selector, which classifies sentences into 4 argumentative moves: PURPOSE, METHODS, RESULTS and CONCLUSION. METHODS: We rely on Bayesian classifiers trained on automatically acquired data. Features representation, selection and weighting are reported and classification effectiveness is evaluated on the four classes using confusion matrices. We also explore the use of simple heuristics to take the position of sentences into account. Recall, precision and F-scores are computed for the CONCLUSION class. For the CONCLUSION class, the F-score reaches 84%. Automatic argumentative classification is feasible on MEDLINE abstracts and should help user navigation in such repositories.

Bayes Theorem↗

PubMed2003

Consistency checks for characterizing protein forms.

Proteomics enforces the reverse chronological order on the gene to protein dogma and imposes amino acid sequences as a starting point of an investigation relative to function. By this approach, proteomics data can confirm the presence of multiple forms of a protein. Notwithstanding variations attributed specific individual features of organisms and tissues, from two to over ten protein forms can be identified in a given sample. The present work describes some guidelines for tracking the origin of alternative protein forms and attempts to tag the details of sequence data in the literature. Working via these guidelines we have uncovered a third alternative form of the Pim subfamily of oncogenes. The term form is here combined with the qualification alternative to describe any product of a given gene including closely related paralogs. This paper also emphasizes the need for consistency checks in annotation processes, such as gene clustering, to avoid losing important details describing protein alternative forms. By identifying alternative protein forms, we illustrate the fact that rationalizing of protein function via the identification of protein-protein interactions should in reality be that of identifying (alternative) form-form interactions.

Amino Acid Sequence↗

PubMedDate not supplied

Deciphering the human nucleolar proteome.

Nucleoli are plurifunctional nuclear domains involved in the regulation of several major cellular processes such as ribosome biogenesis, the biogenesis of non-ribosomal ribonucleoprotein complexes, cell cycle, and cellular aging. Until recently, the protein content of nucleoli was poorly described. Several proteomic analyses have been undertaken to discover the molecular bases of the biological roles fulfilled by nucleoli. These studies have led to the identification of more than 700 proteins. Extensive bibliographic and bioinformatic analyses allowed the classification of the identified proteins into functional groups and suggested potential functions of 150 human proteins previously uncharacterized. The combination of improvements in mass spectrometry technologies, the characterization of protein complexes, and data mining will assist in furthering our understanding of the role of nucleoli in different physiological and pathological cell states.

Cell Nucleolus↗