Chi-square-based scoring function for categorization of MEDLINE citations

Objectives: Text categorization has been used in biomedical informatics for identifying documents containing relevant topics of interest. We developed a simple method that uses a chi-square-based scoring function to determine the likelihood of MEDLINE citations containing genetic relevant topic. Methods: Our procedure requires construction of a genetic and a nongenetic domain document corpus. We used MeSH descriptors assigned to MEDLINE citations for this categorization task. We compared frequencies of MeSH descriptors between two corpora applying chi-square test. A MeSH descriptor was considered to be a positive indicator if its relative observed frequency in the genetic domain corpus was greater than its relative observed frequency in the nongenetic domain corpus. The output of the proposed method is a list of scores for all the citations, with the highest score given to those citations containing MeSH descriptors typical for the genetic domain. Results: Validation was done on a set of 734 manually annotated MEDLINE citations. It achieved predictive accuracy of 0.87 with 0.69 recall and 0.64 precision. We evaluated the method by comparing it to three machine learning algorithms (support vector machines, decision trees, na\"ive Bayes). Although the differences were not statistically significantly different, results showed that our chi-square scoring performs as good as compared machine learning algorithms. Conclusions: We suggest that the chi-square scoring is an effective solution to help categorize MEDLINE citations. The algorithm is implemented in the BITOLA literature-based discovery support system as a preprocessor for gene symbol disambiguation process.Comment: 34 pages, 2 figure

Enhancing navigation in biomedical databases by community voting and database-driven text classification

<p>Abstract</p> <p>Background</p> <p>The breadth of biological databases and their information content continues to increase exponentially. Unfortunately, our ability to query such sources is still often suboptimal. Here, we introduce and apply community voting, database-driven text classification, and visual aids as a means to incorporate distributed expert knowledge, to automatically classify database entries and to efficiently retrieve them.</p> <p>Results</p> <p>Using a previously developed peptide database as an example, we compared several machine learning algorithms in their ability to classify abstracts of published literature results into categories relevant to peptide research, such as related or not related to cancer, angiogenesis, molecular imaging, etc. Ensembles of bagged decision trees met the requirements of our application best. No other algorithm consistently performed better in comparative testing. Moreover, we show that the algorithm produces meaningful class probability estimates, which can be used to visualize the confidence of automatic classification during the retrieval process. To allow viewing long lists of search results enriched by automatic classifications, we added a dynamic heat map to the web interface. We take advantage of community knowledge by enabling users to cast votes in Web 2.0 style in order to correct automated classification errors, which triggers reclassification of all entries. We used a novel framework in which the database "drives" the entire vote aggregation and reclassification process to increase speed while conserving computational resources and keeping the method scalable. In our experiments, we simulate community voting by adding various levels of noise to nearly perfectly labelled instances, and show that, under such conditions, classification can be improved significantly.</p> <p>Conclusion</p> <p>Using PepBank as a model database, we show how to build a classification-aided retrieval system that gathers training data from the community, is completely controlled by the database, scales well with concurrent change events, and can be adapted to add text classification capability to other biomedical databases.</p> <p>The system can be accessed at <url>http://pepbank.mgh.harvard.edu</url>.</p

Discovering semantic features in the literature: a foundation for building functional associations

BACKGROUND: Experimental techniques such as DNA microarray, serial analysis of gene expression (SAGE) and mass spectrometry proteomics, among others, are generating large amounts of data related to genes and proteins at different levels. As in any other experimental approach, it is necessary to analyze these data in the context of previously known information about the biological entities under study. The literature is a particularly valuable source of information for experiment validation and interpretation. Therefore, the development of automated text mining tools to assist in such interpretation is one of the main challenges in current bioinformatics research. RESULTS: We present a method to create literature profiles for large sets of genes or proteins based on common semantic features extracted from a corpus of relevant documents. These profiles can be used to establish pair-wise similarities among genes, utilized in gene/protein classification or can be even combined with experimental measurements. Semantic features can be used by researchers to facilitate the understanding of the commonalities indicated by experimental results. Our approach is based on non-negative matrix factorization (NMF), a machine-learning algorithm for data analysis, capable of identifying local patterns that characterize a subset of the data. The literature is thus used to establish putative relationships among subsets of genes or proteins and to provide coherent justification for this clustering into subsets. We demonstrate the utility of the method by applying it to two independent and vastly different sets of genes. CONCLUSION: The presented method can create literature profiles from documents relevant to sets of genes. The representation of genes as additive linear combinations of semantic features allows for the exploration of functional associations as well as for clustering, suggesting a valuable methodology for the validation and interpretation of high-throughput experimental data

Ermittlung von Zusammenhängen zwischen enzymatischer Aktivität und Krankheiten durch die automatische Analyse wissenschaftlicher Publikationen

Aufgrund des schnellen Wachstums biomedizinischer Daten sowie der assoziierten Literatur wird es auch für Experten zunehmend schwierig, den Überblick über den aktuellen Wissensstand zu behalten. Der Aufbau und die manuelle Erweiterung von Datenbanken ist teuer und zeitaufwändig, kann jedoch durch linguistische Methoden unterstützt werden, welche Erkenntnisse automatisch aus der wissenschaftlichen Literatur extrahieren. Die vorliegende Dissertation stellt eine solche Methode zur Annotation von Enzymklassen mit krankheitsrelevanten Informationen vor. Die Enzymnamen von 3901 Enzymklassen der BRENDA, einer Sammlung von qualitativen und quantitativen Enzymdaten, wurden in einem Textkorpus aus über 100000 Kurzzusammenfassungen der PubMed-Datenbank identifiziert. Phrasen der Kurzzusammenfassungen konnten durch das MetaMap-Programm den Konzepten des UMLS (Unified Medical Language Systems) zugewiesen werden, was eine Identifikation der krankheitsrelevanten Begriffe mittels ihrer semantischen Felder in der UMLS-Ontologie erlaubte. Eine Zuordnung von Enzymklassen zu Krankheitskonzepten erfolgte aufgrund der gemeinsamen Nennung innerhalb eines Satzes. Die Zahl falscher Zuordnung konnte durch den Einsatz verschiedener Filter verringert werden. Verwendet wurden unter anderem die Mindestzahl gemeinsamer Nennungen, die Entfernung von Sätzen mit einer Negation sowie die Klassifikation unbekannter Sätze durch eine Support Vector Machine. Eine Überprüfung der Zuordnungen anhand 1500 manuell annotierter Sätze ergab eine Präzision von 95%, was eine direkte Erweiterung der BRENDA-Datenbank mit den gefundenen Zuordnungen erlaubte

Text Mining and Gene Expression Analysis Towards Combined Interpretation of High Throughput Data

Microarrays can capture gene expression activity for thousands of genes simultaneously and thus make it possible to analyze cell physiology and disease processes on molecular level. The interpretation of microarray gene expression experiments profits from knowledge on the analyzed genes and proteins and the biochemical networks in which they play a role. The trend is towards the development of data analysis methods that integrate diverse data types. Currently, the most comprehensive biomedical knowledge source is a large repository of free text articles. Text mining makes it possible to automatically extract and use information from texts. This thesis addresses two key aspects, biomedical text mining and gene expression data analysis, with the focus on providing high-quality methods and data that contribute to the development of integrated analysis approaches. The work is structured in three parts. Each part begins by providing the relevant background, and each chapter describes the developed methods as well as applications and results. Part I deals with biomedical text mining: Chapter 2 summarizes the relevant background of text mining; it describes text mining fundamentals, important text mining tasks, applications and particularities of text mining in the biomedical domain, and evaluation issues. In Chapter 3, a method for generating high-quality gene and protein name dictionaries is described. The analysis of the generated dictionaries revealed important properties of individual nomenclatures and the used databases (Fundel and Zimmer, 2006). The dictionaries are publicly available via a Wiki, a web service, and several client applications (Szugat et al., 2005). In Chapter 4, methods for the dictionary-based recognition of gene and protein names in texts and their mapping onto unique database identifiers are described. These methods make it possible to extract information from texts and to integrate text-derived information with data from other sources. Three named entity identification systems have been set up, two of them building upon the previously existing tool ProMiner (Hanisch et al., 2003). All of them have shown very good performance in the BioCreAtIvE challenges (Fundel et al., 2005a; Hanisch et al., 2005; Fundel and Zimmer, 2007). In Chapter 5, a new method for relation extraction (Fundel et al., 2007) is presented. It was applied on the largest collection of biomedical literature abstracts, and thus a comprehensive network of human gene and protein relations has been generated. A classification approach (Küffner et al., 2006) can be used to specify relation types further; e. g., as activating, direct physical, or gene regulatory relation. Part II deals with gene expression data analysis: Gene expression data needs to be processed so that differentially expressed genes can be identified. Gene expression data processing consists of several sequential steps. Two important steps are normalization, which aims at removing systematic variances between measurements, and quantification of differential expression by p-value and fold change determination. Numerous methods exist for these tasks. Chapter 6 describes the relevant background of gene expression data analysis; it presents the biological and technical principles of microarrays and gives an overview of the most relevant data processing steps. Finally, it provides a short introduction to osteoarthritis, which is in the focus of the analyzed gene expression data sets. In Chapter 7, quality criteria for the selection of normalization methods are described, and a method for the identification of differentially expressed genes is proposed, which is appropriate for data with large intensity variances between spots representing the same gene (Fundel et al., 2005b). Furthermore, a system is described that selects an appropriate combination of feature selection method and classifier, and thus identifies genes which lead to good classification results and show consistent behavior in different sample subgroups (Davis et al., 2006). The analysis of several gene expression data sets dealing with osteoarthritis is described in Chapter 8. This chapter contains the biomedical analysis of relevant disease processes and distinct disease stages (Aigner et al., 2006a), and a comparison of various microarray platforms and osteoarthritis models. Part III deals with integrated approaches and thus provides the connection between parts I and II: Chapter 9 gives an overview of different types of integrated data analysis approaches, with a focus on approaches that integrate gene expression data with manually compiled data, large-scale networks, or text mining. In Chapter 10, a method for the identification of genes which are consistently regulated and have a coherent literature background (Küffner et al., 2005) is described. This method indicates how gene and protein name identification and gene expression data can be integrated to return clusters which contain genes that are relevant for the respective experiment together with literature information that supports interpretation. Finally, in Chapter 11 ideas on how the described methods can contribute to current research and possible future directions are presented

Bioinspired Materials Design: A Text Mining Approach to Determining Design Principles of Biological Materials

Biological materials are often more efficient and tend to have a wider range and combination of properties than present-day engineered materials. Despite the limited set of components, biological materials are able to achieve great diversity in their material properties by the arrangements of the material components, which form unique structures. The structure-property relationships are known as structural design principles. With the utilization of these design principles, materials designers can develop bioinspired engineered materials with similarly improved effectiveness. While considerable research has been conducted on biological materials, identifying beneficial structural design principles can be time-intensive. To aid materials designers, the research in this dissertation focuses on the development of a text mining algorithm that can quickly identify potential structural design principles of biological materials with respect to a chosen material property or combination of properties. The development of the text mining tool involves four separate stages. The first stage centers on the creation of a basic information retrieval algorithm to extract passages describing property-specific structural design principles from a corpus of materials journal articles. Although the Stage 1 tool identifies over 90% of the principles (recall), only 32% of the returned passages are relevant (precision). The second stage investigates text classification techniques to refine the program in order to improve precision. The classic techniques of machine learning classifiers, statistical features, and part-of-speech analyses, are evaluated for effectiveness in sorting passages into relevant and irrelevant classes. In the third stage, manual identification of patterns in the returned passages is employed to create a rule-based method. The resulting Stage 3 algorithm’s precision values increase to 45%. In the final stage of algorithm development, the manual rule-based classification method is revisited to identify stricter rules to further emphasize precision. The Stage 4 algorithm successfully improves overall precision to 65% and reduces the number of returned passages by 74%, which allows a materials designer to more quickly identify useful principles. Finally, the research concludes with a validation that the text mining tool effectively identifies structural design principles and that the principles can be used in the development of bioinspired materials