Kristina M Hettne

Publications of Kristina M Hettne

• Literature-aided interpretation of gene expression data with the weighted global test.

  Authors: Rob Jelier, Jelle J Goeman, Kristina M Hettne, Martijn J Schuemie, Johan T den Dunnen, Peter A C 't Hoen

  Briefings in bioinformatics. 02/2011; 12(5):518-29.

  Most methods for the interpretation of gene expression profiling experiments rely on the categorization of genes, as provided by the Gene Ontology (GO) and pathway databases. Due to the manualMost methods for the interpretation of gene expression profiling experiments rely on the categorization of genes, as provided by the Gene Ontology (GO) and pathway databases. Due to the manual curation process, such databases are never up-to-date and tend to be limited in focus and coverage. Automated literature mining tools provide an attractive, alternative approach. We review how they can be employed for the interpretation of gene expression profiling experiments. We illustrate that their comprehensive scope aids the interpretation of data from domains poorly covered by GO or alternative databases, and allows for the linking of gene expression with diseases, drugs, tissues and other types of concepts. A framework for proper statistical evaluation of the associations between gene expression values and literature concepts was lacking and is now implemented in a weighted extension of global test. The weights are the literature association scores and reflect the importance of a gene for the concept of interest. In a direct comparison with classical GO-based gene sets, we show that use of literature-based associations results in the identification of much more specific GO categories. We demonstrate the possibilities for linking of gene expression data to patient survival in breast cancer and the action and metabolism of drugs. Coupling with online literature mining tools ensures transparency and allows further study of the identified associations. Literature mining tools are therefore powerful additions to the toolbox for the interpretation of high-throughput genomics data.

• Automatic vs. manual curation of a multi-source chemical dictionary: the impact on text mining.

  Authors: Kristina M Hettne, Antony J Williams, Erik M van Mulligen, Jos Kleinjans, Valery Tkachenko, Jan A Kors

  Journal of cheminformatics. 01/2010; 2(1):4.

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• Rewriting and suppressing UMLS terms for improved biomedical term identification.

  Authors: Kristina M Hettne, Erik M van Mulligen, Martijn J Schuemie, Bob Ja Schijvenaars, Jan A Kors

  Journal of biomedical semantics. 01/2010; 1(1):5.

  ABSTRACT: Identification of terms is essential for biomedical text mining.. We concentrate here on the use of vocabularies for term identification, specifically the Unified Medical Language SystemABSTRACT: Identification of terms is essential for biomedical text mining.. We concentrate here on the use of vocabularies for term identification, specifically the Unified Medical Language System (UMLS). To make the UMLS more suitable for biomedical text mining we implemented and evaluated nine term rewrite and eight term suppression rules. The rules rely on UMLS properties that have been identified in previous work by others, together with an additional set of new properties discovered by our group during our work with the UMLS. Our work complements the earlier work in that we measure the impact on the number of terms identified by the different rules on a MEDLINE corpus. The number of uniquely identified terms and their frequency in MEDLINE were computed before and after applying the rules. The 50 most frequently found terms together with a sample of 100 randomly selected terms were evaluated for every rule. Five of the nine rewrite rules were found to generate additional synonyms and spelling variants that correctly corresponded to the meaning of the original terms and seven out of the eight suppression rules were found to suppress only undesired terms. Using the five rewrite rules that passed our evaluation, we were able to identify 1,117,772 new occurrences of 14,784 rewritten terms in MEDLINE. Without the rewriting, we recognized 651,268 terms belonging to 397,414 concepts; with rewriting, we recognized 666,053 terms belonging to 410,823 concepts, which is an increase of 2.8% in the number of terms and an increase of 3.4% in the number of concepts recognized. Using the seven suppression rules, a total of 257,118 undesired terms were suppressed in the UMLS, notably decreasing its size. 7,397 terms were suppressed in the corpus. We recommend applying the five rewrite rules and seven suppression rules that passed our evaluation when the UMLS is to be used for biomedical term identification in MEDLINE. A software tool to apply these rules to the UMLS is freely available at http://biosemantics.org/casper.

• A Dictionary to Identify Small Molecules and Drugs in Free Text.

  Authors: Kristina M Hettne, Rob H Stierum, Martijn J Schuemie, Peter JM Hendriksen, Bob Ja Schijvenaars, Erik M van Mulligen, Jos Kleinjans, Jan A Kors

  Bioinformatics (Oxford, England). 09/2009;

  MOTIVATION: From the scientific community, a lot of effort has been spent on the correct identification of gene and protein names in text, while less effort has been spent on the correctMOTIVATION: From the scientific community, a lot of effort has been spent on the correct identification of gene and protein names in text, while less effort has been spent on the correct identification of chemical names. Dictionary-based term identification has the power to recognize the diverse representation of chemical information in the literature and map the chemicals to their database identifiers. RESULTS: We developed a dictionary for the identification of small molecules and drugs in text, combining information from UMLS, MeSH, ChEBI, DrugBank, KEGG, HMDB, and ChemIDplus. Rule-based term filtering, manual check of highly frequent terms, and disambiguation rules were applied. We tested the combined dictionary and the dictionaries derived from the individual resources on an annotated corpus, and conclude the following: (1) each of the different processing steps increase precision with a minor loss of recall; (2) the overall performance of the combined dictionary is acceptable (precision 0.67, recall 0.40 (0.80 for trivial names); (3) the combined dictionary performed better than the dictionary in the chemical recognizer OSCAR3; (4) the performance of a dictionary based on ChemIDplus alone is comparable to the performance of the combined dictionary. AVAILABILITY: The combined dictionary is freely available as an XML file in Simple Knowledge Organization System format on the web at http://www.biosemantics.org/chemlist. CONTACT: k.hettne@erasmusmc.nl.

• Model Formulation: Training Multidisciplinary Biomedical Informatics Students: Three Years of Experience.

  Authors: Erik M. van Mulligen, Montserrat Cases, Kristina M. Hettne, Eva Molero, Marc Weeber, Kevin A. Robertson, Baldomero Oliva, Guillermo de la Calle, Victor Maojo

  JAMIA. 01/2008; 15:246-254.

• Automatic mining of the literature to generate new hypotheses for the possible link between periodontitis and atherosclerosis: lipopolysaccharide as a case study.

  Authors: Kristina M Hettne, Marc Weeber, Marja L Laine, Hugo Ten Cate, Scott Boyer, Jan A Kors, Bruno G Loos

  Journal of clinical periodontology. 01/2008; 34(12):1016-24.

  AIM: The aim of the current report was to generate and explore new hypotheses into how, in a pathophysiological sense, atherosclerosis and periodontitis could be linked. MATERIAL AND METHODS: TwoAIM: The aim of the current report was to generate and explore new hypotheses into how, in a pathophysiological sense, atherosclerosis and periodontitis could be linked. MATERIAL AND METHODS: Two different biomedical informatics techniques were used: an association-based technique that generated a ranked list of genes associated with the diseases, and a natural language processing tool that extracted the relationships between the retrieved genes and lipopolysaccharide (LPS). RESULTS: This combined approach of association-based and natural language processing-based literature mining identified a hit list of 16 candidate genes, with PON1 as the primary candidate. CONCLUSIONS: Further study of the literature prompted the hypothesis that PON1 might connect periodontitis with atherosclerosis in both an LPS-dependent and a non-LPS-dependent manner. Furthermore, the resulting genes not only confirmed already known associations between the two diseases, but also provided genes or gene products that have only been investigated separately in the two disease states, and genes or gene products previously reported to be involved in atherosclerosis. These findings remain to be investigated through clinical studies. This example of multidisciplinary research illustrates how collaborative efforts of investigators from different fields of expertise can result in the discovery of new hypotheses.

• Applied information retrieval and multidisciplinary research: new mechanistic hypotheses in complex regional pain syndrome.

  Authors: Kristina M Hettne, Marissa de Mos, Anke G J de Bruijn, Marc Weeber, Scott Boyer, Erik M van Mulligen, Montserrat Cases, Jordi Mestres, Johan van der Lei

  Journal of biomedical discovery and collaboration. 02/2007; 2:2.

  BACKGROUND: Collaborative efforts of physicians and basic scientists are often necessary in the investigation of complex disorders. Difficulties can arise, however, when large amounts of informationBACKGROUND: Collaborative efforts of physicians and basic scientists are often necessary in the investigation of complex disorders. Difficulties can arise, however, when large amounts of information need to reviewed. Advanced information retrieval can be beneficial in combining and reviewing data obtained from the various scientific fields. In this paper, a team of investigators with varying backgrounds has applied advanced information retrieval methods, in the form of text mining and entity relationship tools, to review the current literature, with the intention to generate new insights into the molecular mechanisms underlying a complex disorder. As an example of such a disorder the Complex Regional Pain Syndrome (CRPS) was chosen. CRPS is a painful and debilitating syndrome with a complex etiology that is still unraveled for a considerable part, resulting in suboptimal diagnosis and treatment. RESULTS: A text mining based approach combined with a simple network analysis identified Nuclear Factor kappa B (NFkappaB) as a possible central mediator in both the initiation and progression of CRPS. CONCLUSION: The result shows the added value of a multidisciplinary approach combined with information retrieval in hypothesis discovery in biomedical research. The new hypothesis, which was derived in silico, provides a framework for further mechanistic studies into the underlying molecular mechanisms of CRPS and requires evaluation in clinical and epidemiological studies.