Computing and Information Systems - Research Publications

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    Stratification bias in low signal microarray studies
    Parker, BJ ; Guenter, S ; Bedo, J (BMC, 2007-09-02)
    BACKGROUND: When analysing microarray and other small sample size biological datasets, care is needed to avoid various biases. We analyse a form of bias, stratification bias, that can substantially affect analyses using sample-reuse validation techniques and lead to inaccurate results. This bias is due to imperfect stratification of samples in the training and test sets and the dependency between these stratification errors, i.e. the variations in class proportions in the training and test sets are negatively correlated. RESULTS: We show that when estimating the performance of classifiers on low signal datasets (i.e. those which are difficult to classify), which are typical of many prognostic microarray studies, commonly used performance measures can suffer from a substantial negative bias. For error rate this bias is only severe in quite restricted situations, but can be much larger and more frequent when using ranking measures such as the receiver operating characteristic (ROC) curve and area under the ROC (AUC). Substantial biases are shown in simulations and on the van 't Veer breast cancer dataset. The classification error rate can have large negative biases for balanced datasets, whereas the AUC shows substantial pessimistic biases even for imbalanced datasets. In simulation studies using 10-fold cross-validation, AUC values of less than 0.3 can be observed on random datasets rather than the expected 0.5. Further experiments on the van 't Veer breast cancer dataset show these biases exist in practice. CONCLUSION: Stratification bias can substantially affect several performance measures. In computing the AUC, the strategy of pooling the test samples from the various folds of cross-validation can lead to large biases; computing it as the average of per-fold estimates avoids this bias and is thus the recommended approach. As a more general solution applicable to other performance measures, we show that stratified repeated holdout and a modified version of k-fold cross-validation, balanced, stratified cross-validation and balanced leave-one-out cross-validation, avoids the bias. Therefore for model selection and evaluation of microarray and other small biological datasets, these methods should be used and unstratified versions avoided. In particular, the commonly used (unbalanced) leave-one-out cross-validation should not be used to estimate AUC for small datasets.
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    BioCaster: detecting public health rumors with a Web-based text mining system.
    Collier, N ; Doan, S ; Kawazoe, A ; Goodwin, RM ; Conway, M ; Tateno, Y ; Ngo, Q-H ; Dien, D ; Kawtrakul, A ; Takeuchi, K ; Shigematsu, M ; Taniguchi, K (Oxford University Press (OUP), 2008-12-15)
    SUMMARY: BioCaster is an ontology-based text mining system for detecting and tracking the distribution of infectious disease outbreaks from linguistic signals on the Web. The system continuously analyzes documents reported from over 1700 RSS feeds, classifies them for topical relevance and plots them onto a Google map using geocoded information. The background knowledge for bridging the gap between Layman's terms and formal-coding systems is contained in the freely available BioCaster ontology which includes information in eight languages focused on the epidemiological role of pathogens as well as geographical locations with their latitudes/longitudes. The system consists of four main stages: topic classification, named entity recognition (NER), disease/location detection and event recognition. Higher order event analysis is used to detect more precisely specified warning signals that can then be notified to registered users via email alerts. Evaluation of the system for topic recognition and entity identification is conducted on a gold standard corpus of annotated news articles. AVAILABILITY: The BioCaster map and ontology are freely available via a web portal at http://www.biocaster.org.
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    Towards a semantic lexicon for biological language processing
    Verspoor, K (HINDAWI LTD, 2005-02-01)
    This paper explores the use of the resources in the National Library of Medicine's Unified Medical Language System (UMLS) for the construction of a lexicon useful for processing texts in the field of molecular biology. A lexicon is constructed from overlapping terms in the UMLS SPECIALIST lexicon and the UMLS Metathesaurus to obtain both morphosyntactic and semantic information for terms, and the coverage of a domain corpus is assessed. Over 77% of tokens in the domain corpus are found in the constructed lexicon, validating the lexicon's coverage of the most frequent terms in the domain and indicating that the constructed lexicon is potentially an important resource for biological text processing.
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    Uncovering protein interaction in abstracts and text using a novel linear model and word proximity networks
    Abi-Haidar, A ; Kaur, J ; Maguitman, A ; Radivojac, P ; Rechtsteiner, A ; Verspoor, K ; Wang, Z ; Rocha, LM (BMC, 2008-01-01)
    BACKGROUND: We participated in three of the protein-protein interaction subtasks of the Second BioCreative Challenge: classification of abstracts relevant for protein-protein interaction (interaction article subtask [IAS]), discovery of protein pairs (interaction pair subtask [IPS]), and identification of text passages characterizing protein interaction (interaction sentences subtask [ISS]) in full-text documents. We approached the abstract classification task with a novel, lightweight linear model inspired by spam detection techniques, as well as an uncertainty-based integration scheme. We also used a support vector machine and singular value decomposition on the same features for comparison purposes. Our approach to the full-text subtasks (protein pair and passage identification) includes a feature expansion method based on word proximity networks. RESULTS: Our approach to the abstract classification task (IAS) was among the top submissions for this task in terms of measures of performance used in the challenge evaluation (accuracy, F-score, and area under the receiver operating characteristic curve). We also report on a web tool that we produced using our approach: the Protein Interaction Abstract Relevance Evaluator (PIARE). Our approach to the full-text tasks resulted in one of the highest recall rates as well as mean reciprocal rank of correct passages. CONCLUSION: Our approach to abstract classification shows that a simple linear model, using relatively few features, can generalize and uncover the conceptual nature of protein-protein interactions from the bibliome. Because the novel approach is based on a rather lightweight linear model, it can easily be ported and applied to similar problems. In full-text problems, the expansion of word features with word proximity networks is shown to be useful, although the need for some improvements is discussed.
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    Ontology quality assurance through analysis of term transformations
    Verspoor, K ; Dvorkin, D ; Cohen, KB ; Hunter, L (OXFORD UNIV PRESS, 2009-06-15)
    MOTIVATION: It is important for the quality of biological ontologies that similar concepts be expressed consistently, or univocally. Univocality is relevant for the usability of the ontology for humans, as well as for computational tools that rely on regularity in the structure of terms. However, in practice terms are not always expressed consistently, and we must develop methods for identifying terms that are not univocal so that they can be corrected. RESULTS: We developed an automated transformation-based clustering methodology for detecting terms that use different linguistic conventions for expressing similar semantics. These term sets represent occurrences of univocality violations. Our method was able to identify 67 examples of univocality violations in the Gene Ontology. AVAILABILITY: The identified univocality violations are available upon request. We are preparing a release of an open source version of the software to be available at http://bionlp.sourceforge.net.
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    The textual characteristics of traditional and Open Access scientific journals are similar
    Verspoor, K ; Cohen, KB ; Hunter, L (BMC, 2009-06-15)
    BACKGROUND: Recent years have seen an increased amount of natural language processing (NLP) work on full text biomedical journal publications. Much of this work is done with Open Access journal articles. Such work assumes that Open Access articles are representative of biomedical publications in general and that methods developed for analysis of Open Access full text publications will generalize to the biomedical literature as a whole. If this assumption is wrong, the cost to the community will be large, including not just wasted resources, but also flawed science. This paper examines that assumption. RESULTS: We collected two sets of documents, one consisting only of Open Access publications and the other consisting only of traditional journal publications. We examined them for differences in surface linguistic structures that have obvious consequences for the ease or difficulty of natural language processing and for differences in semantic content as reflected in lexical items. Regarding surface linguistic structures, we examined the incidence of conjunctions, negation, passives, and pronominal anaphora, and found that the two collections did not differ. We also examined the distribution of sentence lengths and found that both collections were characterized by the same mode. Regarding lexical items, we found that the Kullback-Leibler divergence between the two collections was low, and was lower than the divergence between either collection and a reference corpus. Where small differences did exist, log likelihood analysis showed that they were primarily in the area of formatting and in specific named entities. CONCLUSION: We did not find structural or semantic differences between the Open Access and traditional journal collections.
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    Protein annotation as term categorization in the gene ontology using word proximity networks
    Verspoor, K ; Cohn, J ; Joslyn, C ; Mniszewski, S ; Rechtsteiner, A ; Rocha, LM ; Simas, T (BMC, 2005-05-24)
    BACKGROUND: We participated in the BioCreAtIvE Task 2, which addressed the annotation of proteins into the Gene Ontology (GO) based on the text of a given document and the selection of evidence text from the document justifying that annotation. We approached the task utilizing several combinations of two distinct methods: an unsupervised algorithm for expanding words associated with GO nodes, and an annotation methodology which treats annotation as categorization of terms from a protein's document neighborhood into the GO. RESULTS: The evaluation results indicate that the method for expanding words associated with GO nodes is quite powerful; we were able to successfully select appropriate evidence text for a given annotation in 38% of Task 2.1 queries by building on this method. The term categorization methodology achieved a precision of 16% for annotation within the correct extended family in Task 2.2, though we show through subsequent analysis that this can be improved with a different parameter setting. Our architecture proved not to be very successful on the evidence text component of the task, in the configuration used to generate the submitted results. CONCLUSION: The initial results show promise for both of the methods we explored, and we are planning to integrate the methods more closely to achieve better results overall.
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    Reuse of terminological resources for efficient ontological engineering in Life Sciences
    Jimeno-Yepes, A ; Jimenez-Ruiz, E ; Berlanga-Llavori, R ; Rebholz-Schuhmann, D (BMC, 2009-01-01)
    This paper is intended to explore how to use terminological resources for ontology engineering. Nowadays there are several biomedical ontologies describing overlapping domains, but there is not a clear correspondence between the concepts that are supposed to be equivalent or just similar. These resources are quite precious but their integration and further development are expensive. Terminologies may support the ontological development in several stages of the lifecycle of the ontology; e.g. ontology integration. In this paper we investigate the use of terminological resources during the ontology lifecycle. We claim that the proper creation and use of a shared thesaurus is a cornerstone for the successful application of the Semantic Web technology within life sciences. Moreover, we have applied our approach to a real scenario, the Health-e-Child (HeC) project, and we have evaluated the impact of filtering and re-organizing several resources. As a result, we have created a reference thesaurus for this project, named HeCTh.
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    Data-poor categorization and passage retrieval for gene ontology annotation in Swiss-Prot
    Ehrler, F ; Geissbuhler, A ; Jimeno, A ; Ruch, P (BMC, 2005-05-24)
    BACKGROUND: In the context of the BioCreative competition, where training data were very sparse, we investigated two complementary tasks: 1) given a Swiss-Prot triplet, containing a protein, a GO (Gene Ontology) term and a relevant article, extraction of a short passage that justifies the GO category assignment; 2) given a Swiss-Prot pair, containing a protein and a relevant article, automatic assignment of a set of categories. METHODS: Sentence is the basic retrieval unit. Our classifier computes a distance between each sentence and the GO category provided with the Swiss-Prot entry. The Text Categorizer computes a distance between each GO term and the text of the article. Evaluations are reported both based on annotator judgements as established by the competition and based on mean average precision measures computed using a curated sample of Swiss-Prot. RESULTS: Our system achieved the best recall and precision combination both for passage retrieval and text categorization as evaluated by official evaluators. However, text categorization results were far below those in other data-poor text categorization experiments The top proposed term is relevant in less that 20% of cases, while categorization with other biomedical controlled vocabulary, such as the Medical Subject Headings, we achieved more than 90% precision. We also observe that the scoring methods used in our experiments, based on the retrieval status value of our engines, exhibits effective confidence estimation capabilities. CONCLUSION: From a comparative perspective, the combination of retrieval and natural language processing methods we designed, achieved very competitive performances. Largely data-independent, our systems were no less effective that data-intensive approaches. These results suggests that the overall strategy could benefit a large class of information extraction tasks, especially when training data are missing. However, from a user perspective, results were disappointing. Further investigations are needed to design applicable end-user text mining tools for biologists.
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    Annotation of protein residues based on a literature analysis: cross-validation against UniProtKb
    Nagel, K ; Jimeno-Yepes, A ; Rebholz-Schuhmann, D (BMC, 2009-01-01)
    BACKGROUND: A protein annotation database, such as the Universal Protein Resource knowledge base (UniProtKb), is a valuable resource for the validation and interpretation of predicted 3D structure patterns in proteins. Existing studies have focussed on point mutation extraction methods from biomedical literature which can be used to support the time consuming work of manual database curation. However, these methods were limited to point mutation extraction and do not extract features for the annotation of proteins at the residue level. RESULTS: This work introduces a system that identifies protein residues in MEDLINE abstracts and annotates them with features extracted from the context written in the surrounding text. MEDLINE abstract texts have been processed to identify protein mentions in combination with taxonomic species and protein residues (F1-measure 0.52). The identified protein-species-residue triplets have been validated and benchmarked against reference data resources (UniProtKb, average F1-measure of 0.54). Then, contextual features were extracted through shallow and deep parsing and the features have been classified into predefined categories (F1-measure ranges from 0.15 to 0.67). Furthermore, the feature sets have been aligned with annotation types in UniProtKb to assess the relevance of the annotations for ongoing curation projects. Altogether, the annotations have been assessed automatically and manually against reference data resources. CONCLUSION: This work proposes a solution for the automatic extraction of functional annotation for protein residues from biomedical articles. The presented approach is an extension to other existing systems in that a wider range of residue entities are considered and that features of residues are extracted as annotations.