2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text.

Functional annotation of proteins is a fundamental problem in the post-genomic era. The recent availability of protein interaction networks for many model species has spurred on the development of computational methods for interpreting such data in order to elucidate protein function. In this review, we describe the current computational approaches for the task, including direct methods, which propagate functional information through the network, and module-assisted methods, which infer functional modules within the network and use those for the annotation task. Although a broad variety of interesting approaches has been developed, further progress in the field will depend on systematic evaluation of the methods and their dissemination in the biological community.

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Network-based prediction of protein function

During a decade of proof-of-principle analysis in model organisms, protein networks have been used to further the study of molecular evolution, to gain insight into the robustness of cells to perturbation, and for assignment of new protein functions. Following these analyses, and with the recent rise of protein interaction measurements in mammals, protein networks are increasingly serving as tools to unravel the molecular basis of disease. We review promising applications of protein networks to disease in four major areas: identifying new disease genes; the study of their network properties; identifying disease-related subnetworks; and network-based disease classification. Applications in infectious disease, personalized medicine, and pharmacology are also forthcoming as the available protein network information improves in quality and coverage.

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Protein networks in disease.

The volume of published biomedical research, and therefore the underlying biomedical knowledge base, is expanding at an increasing rate. Among the tools that can aid researchers in coping with this information overload are text mining and knowledge extraction. Significant progress has been made in applying text mining to named entity recognition, text classification, terminology extraction, relationship extraction and hypothesis generation. Several research groups are constructing integrated flexible text-mining systems intended for multiple uses. The major challenge of biomedical text mining over the next 5–10 years is to make these systems useful to biomedical researchers. This will require enhanced access to full text, better understanding of the feature space of biomedical literature, better methods for measuring the usefulness of systems to users, and continued cooperation with the biomedical research community to ensure that their needs are addressed.

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A survey of current work in biomedical text mining

We have developed Textpresso, a new text-mining system for scientific literature whose capabilities go far beyond those of a simple keyword search engine Textpresso's two major elements are a collection of the full text of scientific articles split into individual sentences, and the implementation of categories of terms for which a database of articles and individual sentences can be searched The categories are classes of biological concepts (eg, gene, allele, cell or cell group, phenotype, etc) and classes that relate two objects (eg, association, regulation, etc) or describe one (eg, biological process, etc) Together they form a catalog of types of objects and concepts called an ontology After this ontology is populated with terms, the whole corpus of articles and abstracts is marked up to identify terms of these categories The current ontology comprises 33 categories of terms A search engine enables the user to search for one or a combination of these tags and/or keywords within a sentence or document, and as the ontology allows word meaning to be queried, it is possible to formulate semantic queries Full text access increases recall of biological data types from 45% to 95% Extraction of particular biological facts, such as gene-gene interactions, can be accelerated significantly by ontologies, with Textpresso automatically performing nearly as well as expert curators to identify sentences; in searches for two uniquely named genes and an interaction term, the ontology confers a 3-fold increase of search efficiency Textpresso currently focuses on Caenorhabditis elegans literature, with 3,800 full text articles and 16,000 abstracts The lexicon of the ontology contains 14,500 entries, each of which includes all versions of a specific word or phrase, and it includes all categories of the Gene Ontology database Textpresso is a useful curation tool, as well as search engine for researchers, and can readily be extended to other organism-specific corpora of text Textpresso can be accessed at http://wwwtextpressoorg or via WormBase at http://wwwwormbaseorg

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Textpresso: an ontology-based information retrieval and extraction system for biological literature.

Motivation Large scale gene expression data are often analysed by clustering genes based on gene expression data alone, though a priori knowledge in the form of biological networks is available. The use of this additional information promises to improve exploratory analysis considerably. Results We propose constructing a distance function which combines information from expression data and biological networks. Based on this function, we compute a joint clustering of genes and vertices of the network. This general approach is elaborated for metabolic networks. We define a graph distance function on such networks and combine it with a correlation-based distance function for gene expression measurements. A hierarchical clustering and an associated statistical measure is computed to arrive at a reasonable number of clusters. Our method is validated using expression data of the yeast diauxic shift. The resulting clusters are easily interpretable in terms of the biochemical network and the gene expression data and suggest that our method is able to automatically identify processes that are relevant under the measured conditions.

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Co-clustering of biological networks and gene expression data.

Identification of gene and protein names in biomedical text is a challenging task as the corresponding nomenclature has evolved over time. This has led to multiple synonyms for individual genes and proteins, as well as names that may be ambiguous with other gene names or with general English words. The Gene List Task of the BioCreAtIvE challenge evaluation enables comparison of systems addressing the problem of protein and gene name identification on common benchmark data. The ProMiner system uses a pre-processed synonym dictionary to identify potential name occurrences in the biomedical text and associate protein and gene database identifiers with the detected matches. It follows a rule-based approach and its search algorithm is geared towards recognition of multi-word names [1]. To account for the large number of ambiguous synonyms in the considered organisms, the system has been extended to use specific variants of the detection procedure for highly ambiguous and case-sensitive synonyms. Based on all detected synonyms for one abstract, the most plausible database identifiers are associated with the text. Organism specificity is addressed by a simple procedure based on additionally detected organism names in an abstract. The extended ProMiner system has been applied to the test cases of the BioCreAtIvE competition with highly encouraging results. In blind predictions, the system achieved an F-measure of approximately 0.8 for the organisms mouse and fly and about 0.9 for the organism yeast.

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ProMiner: rule-based protein and gene entity recognition

A growing body of work is devoted to the extraction of protein or gene interaction information from the scientific literature. Yet, the basis for most extraction algorithms, i.e. the specific and sensitive recognition of protein and gene names and their numerous synonyms, has not been adequately addressed. Here we describe the construction of a comprehensive general purpose name dictionary and an accompanying automatic curation procedure based on a simple token model of protein names. We designed an efficient search algorithm to analyze all abstracts in MEDLINE in a reasonable amount of time on standard computers. The parameters of our method are optimized using machine learning techniques. Used in conjunction, these ingredients lead to good search performance. A supplementary web page is available at http://cartan.gmd.de/ProMiner/.

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Playing biology's name game: identifying protein names in scientific text.

Summary: Biological networks, such as protein interaction, regulatory or metabolic networks, derived from public databases, biological experiments or text mining can be useful for the analysis of high-throughput experimental data. We present two algorithms embedded in the ToPNet application that show promising performance in analyzing expression data in the context of such networks. First, the Significant Area Search algorithm detects subnetworks consisting of significantly regulated genes. These subnetworks often provide hints on which biological processes are affected in the measured conditions. Second, Pathway Queries allow detection of networks including molecules that are not necessarily significantly regulated, such as transcription factors or signaling proteins. Moreover, using these queries, the user can formulate biological hypotheses and check their validity with respect to experimental data. All resulting networks and pathways can be explored further using the interactive analysis tools provided by ToPNet program.

New methods for joint analysis of biological networks and expression data

Background: Despite considerable limitations such as low sensitivity and insensitivity to alternative splicing, posttranscriptional regulation, and posttranslational modification, cDNA array technology provides a powerful tool with which to obtain an overview of gene expression patterns, hardly achievable with other techniques. This has been shown to be true for the analysis of known genes as well as the discovery of new genes of interest.

Methods: Samples of normal and late-stage osteoarthritic cartilage of human knee joints were analyzed with use of the Human Cancer 1.2 cDNA-array and TaqMan analysis.

Results: In spite of a large variability of expression levels among different patients, significant expression patterns for many known genes of interest such as cartilage matrix proteins (e.g., collagen types II, VI, and XI; aggrecan; decorin; biglycan) and matrix-degrading proteases were detected. Of the latter, MMP-3 appeared to be strongly expressed in normal and early degenerative cartilage and downregulated in the late disease stages. This indicates that, in the late stages of cartilage degeneration, other degradation pathways might be more important, for example, those involving enzymes such as MMP-2 and MMP-13, both of which were upregulated in late-stage disease.

Conclusion: Most results have to be considered to be preliminary to a certain degree, as technical tools and interpretation approaches are still emerging and need more validation. Clearly, there is a major challenge to distill information and knowledge out of the obtained mass of data. However, these data will be one basis of a new world of biological understanding. These new insights will be network-based and no longer molecule-centered. Today, molecules have a biochemical and physiological context; tomorrow, biological networks will have molecules as constituents.

Daniel Hanisch

Papers

Co-clustering of biological networks and gene expression data.

ProMiner: rule-based protein and gene entity recognition

Playing biology's name game: identifying protein names in scientific text.

New methods for joint analysis of biological networks and expression data

Gene Expression in Chondrocytes Assessed with Use of Microarrays