Extract Biological Information Research Articles

Towards fully automated identification of vesicle-membrane fusion events in TIRF microscopy

Total internal reflection fluorescence microscopy (TIRF-M) is imposing itself as the tool of choice for studying biological activity in close proximity to the plasma membrane. For example, the exquisite selectivity of TIRF-M allows monitoring the movement of GFP-tagged vesicles and their recruitment to the plasma membrane of cells. We present a novel computer vision system for automatically identifying elusive vesicle-plasma membrane fusion events. Our method is based on robust object tracking and matched filtering. It should accelerate the quantification of TIRF-M data and allow the extraction of more biological information from image data to support research in diabetes and obesity.

Introducing meta-services for biomedical information extraction

We introduce the first meta-service for information extraction in molecular biology, the BioCreative MetaServer (BCMS; ). This prototype platform is a joint effort of 13 research groups and provides automatically generated annotations for PubMed/Medline abstracts. Annotation types cover gene names, gene IDs, species, and protein-protein interactions. The annotations are distributed by the meta-server in both human and machine readable formats (HTML/XML). This service is intended to be used by biomedical researchers and database annotators, and in biomedical language processing. The platform allows direct comparison, unified access, and result aggregation of the annotations.

Linking genes to literature: text mining, information extraction, and retrieval applications for biology

Efficient access to information contained in online scientific literature collections is essential for life science research, playing a crucial role from the initial stage of experiment planning to the final interpretation and communication of the results. The biological literature also constitutes the main information source for manual literature curation used by expert-curated databases. Following the increasing popularity of web-based applications for analyzing biological data, new text-mining and information extraction strategies are being implemented. These systems exploit existing regularities in natural language to extract biologically relevant information from electronic texts automatically. The aim of the BioCreative challenge is to promote the development of such tools and to provide insight into their performance. This review presents a general introduction to the main characteristics and applications of currently available text-mining systems for life sciences in terms of the following: the type of biological information demands being addressed; the level of information granularity of both user queries and results; and the features and methods commonly exploited by these applications. The current trend in biomedical text mining points toward an increasing diversification in terms of application types and techniques, together with integration of domain-specific resources such as ontologies. Additional descriptions of some of the systems discussed here are available on the internet .

Integrating Bioinformatics and Computational Biology: Perspectives and Possibilities for In Silico Network Reconstruction in Molecular Systems Biology

There is a flood of molecular data about many aspects of cellular functioning. This data ranges from sequence and structural data to gene and protein regulation data, including time dependent changes in the concentration. Integration of the different datasets through computational methods is required to extract biological information that is relevant from a systems biology perspective. In this paper we discuss how different computational tools and methods can be made to work together integrating different types of data, mining these data for biological information, and assisting in pathway reconstruction and biological hypotheses generation. We review the recent body of literature where such integrative approaches are used and discuss automation of data integration and model building to generate testable biological hypotheses. We analyze issues regarding the design of such automated tools and discuss what limitations and pitfalls can be foreseen for the automation and what solutions can computer science and biologists provide to overcome them.

A Computational Strategy to Analyze Label-Free Temporal Bottom-Up Proteomics Data

Biological systems are in a continual state of flux, which necessitates an understanding of the dynamic nature of protein abundances. The study of protein abundance dynamics has become feasible with recent improvements in mass spectrometry-based quantitative proteomics. However, a number of challenges still remain related to how best to extract biological information from dynamic proteomics data, for example, challenges related to extraneous variability, missing abundance values, and the identification of significant temporal patterns. This paper describes a strategy that addresses these issues and demonstrates its values for analyzing temporal bottom-up proteomics data using data from a Rhodobacter sphaeroides 2.4.1 time-course study.

Evidence of probabilistic behaviour in protein interaction networks

BackgroundData from high-throughput experiments of protein-protein interactions are commonly used to probe the nature of biological organization and extract functional relationships between sets of proteins. What has not been appreciated is that the underlying mechanisms involved in assembling these networks may exhibit considerable probabilistic behaviour.ResultsWe find that the probability of an interaction between two proteins is generally proportional to the numerical product of their individual interacting partners, or degrees. The degree-weighted behaviour is manifested throughout the protein-protein interaction networks studied here, except for the high-degree, or hub, interaction areas. However, we find that the probabilities of interaction between the hubs are still high. Further evidence is provided by path length analyses, which show that these hubs are separated by very few links.ConclusionThe results suggest that protein-protein interaction networks incorporate probabilistic elements that lead to scale-rich hierarchical architectures. These observations seem to be at odds with a biologically-guided organization. One interpretation of the findings is that we are witnessing the ability of proteins to indiscriminately bind rather than the protein-protein interactions that are actually utilized by the cell in biological processes. Therefore, the topological study of a degree-weighted network requires a more refined methodology to extract biological information about pathways, modules, or other inferred relationships among proteins.

Evaluation of text-mining systems for biology: overview of the Second BioCreative community challenge

Background:Genome sciences have experienced an increasing demand for efficient text-processing tools that can extract biologically relevant information from the growing amount of published literature. In response, a range of text-mining and information-extraction tools have recently been developed specifically for the biological domain. Such tools are only useful if they are designed to meet real-life tasks and if their performance can be estimated and compared. The BioCreative challenge (Critical Assessment of Information Extraction in Biology) consists of a collaborative initiative to provide a common evaluation framework for monitoring and assessing the state-of-the-art of text-mining systems applied to biologically relevant problems.Results:The Second BioCreative assessment (2006 to 2007) attracted 44 teams from 13 countries worldwide, with the aim of evaluating current information-extraction/text-mining technologies developed for one or more of the three tasks defined for this challenge evaluation. These tasks included the recognition of gene mentions in abstracts (gene mention task); the extraction of a list of unique identifiers for human genes mentioned in abstracts (gene normalization task); and finally the extraction of physical protein-protein interaction annotation-relevant information (protein-protein interaction task). The 'gold standard' data used for evaluating submissions for the third task was provided by the interaction databases MINT (Molecular Interaction Database) and IntAct.Conclusion:The Second BioCreative assessment almost doubled the number of participants for each individual task when compared with the first BioCreative assessment. An overall improvement in terms of balanced precision and recall was observed for the best submissions for the gene mention (F score 0.87); for the gene normalization task, the best results were comparable (F score 0.81) compared with results obtained for similar tasks posed at the first BioCreative challenge. In case of the protein-protein interaction task, the importance and difficulties of experimentally confirmed annotation extraction from full-text articles were explored, yielding different results depending on the step of the annotation extraction workflow. A common characteristic observed in all three tasks was that the combination of system outputs could yield better results than any single system. Finally, the development of the first text-mining meta-server was promoted within the context of this community challenge.

Activation of MYC Pathway Is a Unifying Pathological Event in the Progression from Monoclonal Gammopathy of Undetermined Significance (MGUS) to Myeloma (MM).

Events mediating transformation from the pre-malignant MGUS to MM is largely unknown. Previous studies have highlighted RAS mutation as a progression event. However, this abnormality is present in only a small subset of patients. Global gene expression differences between MGUS and MM have been reported but provided little coherent biological insights. To define progression events, we examined the differences in gene expression profiles (GEP, Affymetrix U133A genechip) between 101 MM and 22 MGUS using a powerful functional genomic tool, Gene Set Enrichment Analysis (Subramanium et al PNAS 2005), that allow extraction of biological information based on enrichment of genes associated with a phenotype within gene-sets derived experimentally or extracted from database. We found that a predominant theme relating to MM progression is the over-expression of proliferation related genes in MM highlighted by the enrichment of cell cycle related gene-sets. Another common theme is the enrichment of gene-sets relating to MYC activation. Seven of eight gene-sets related to MYC activation in the database are significantly enriched (p-value <0.05 and false detection rate <0.05). Leading edge (enriched genes) analysis of enriched MYC-related and proliferation-related gene-sets revealed a core, overlapping signature of 8 genes specific to MYC and not proliferation. These genes are further verified as MYC transcriptional targets using Metacore, a network analysis tool. In addition, 7 of the 8 genes had been validated as MYC target genes by chromatin immunoprecipitation previously (www.myccancergene.org). We validated this signature in independent publicly available cell lines and mouse gene expression experiments where this signature is present in mammary epithelial cell lines engineered to over-express MYC but not E2F3, SRC and BCAT, and mouse embryonic fibroblast over-expressing MYC but not HER2 or with RB knocked-out. In addition, analysis of proliferation index in these datasets showed that there is dissociation of proliferation from this signature. This showed conclusively that our signature is MYC specific and not related to proliferation. Of interest, the signature is also present in cell lines with activating RAS mutation, which also have high expression of MYC, suggesting that MYC activation is a downstream effect of activated RAS signaling. Indeed, in our patient cohort, most patients with RAS mutation express this signature. When applied to 2 large GEP MM patient cohorts, this signature was not present in normal plasma cells, rarely present in MGUS, and present in a significant proportion of MM and almost all the human myeloma cell lines. When analyzed in the context of other B-cell malignancies, the signature is very strong in Burkitt's lymphoma, present in most myeloma but absent in chronic lymphocytic leukemia, Waldenstrom Macroglobulinemia, and normal B-cells. Our analysis therefore suggests that MYC activation may be a unifying pathological event in MGUS to MM progression. As MYC translocations occur in only about 13% of newly diagnosed MM patients, activating RAS mutations and other trans-mechanisms may mediate MYC activation. Our recent demonstration that sporadic MYC activation in a mouse strain prone to monoclonal gammopathy led to MM development provides important functional proof.

User-Centered Evaluation of Arizona BioPathway: An Information Extraction, Integration, and Visualization System

Explosive growth in biomedical research has made automated information extraction, knowledge integration, and visualization increasingly important and critically needed. The Arizona BioPathway (ABP) system extracts and displays biological regulatory pathway information from the abstracts of journal articles. This study uses relations extracted from more than 200 PubMed abstracts presented in a tabular and graphical user interface with built-in search and aggregation functionality. This paper presents a task-centered assessment of the usefulness and usability of the ABP system focusing on its relation aggregation and visualization functionalities. Results suggest that our graph-based visualization is more efficient in supporting pathway analysis tasks and is perceived as more useful and easier to use as compared to a text-based literature-viewing method. Relation aggregation significantly contributes to knowledge-acquisition efficiency. Together, the graphic and tabular views in the ABP Visualizer provide a flexible and effective interface for pathway relation browsing and analysis. Our study contributes to pathway-related research and biological information extraction by assessing the value of a multiview, relation-based interface that supports user-controlled exploration of pathway information across multiple granularities.

XGENIA: A comprehensive OWL ontology based on the GENIA corpus.

The GENIA ontology is a taxonomy that was developed as a result of manual annotation of a subset of MEDLINE, the GENIA corpus. Both the ontology and corpus have been used as a benchmark to test and develop biological information extraction tools. Recent work shows, however, that there is a demand for a more comprehensive ontology that would go along with the corpus. We propose a complete OWL ontology built on top of the GENIA ontology utilizing the GENIA corpus. The proposed ontology includes elements such as the original taxonomy of categories, biological entities as individuals, relations between individuals using verbs and verb nominalizations as object properties, and links to the UMLS Metathesaurus concepts. http://www.ece.ualberta.ca/~rrak/ontology/xGENIA/

Network motif analysis of a multi-mode genetic-interaction network

Statistical and computational methods for the extraction of biological information from dense multi-mode genetic-interaction networks were developed and implemented in open-source software.

Finding module-based gene networks with state-space models - Mining high-dimensional and short time-course gene expression data

This study explores some problems to analyze time-course gene expression data by state-space models (SSMs). One problem is regarding the methods of parameter estimation and determination of the dimension of the internal state variable. Although several methods have been applied, there are few literature studies which with to compare them. Thus, this paper gives a brief review of the existing literature that use the SSM to analyze the gene expression time-course data. Another problem is the identifiability of the model. If the parameters of SSMs are simply estimated without any constraints for parameter space, they lack identifiability. To identify a system uniquely, it requires a specific algorithm to estimate the parameters with some constraints. For that purpose, an identifiable form of SSMs and an algorithm for estimating parameters are derived. The last problem is the extraction of biological information by interpreting the estimated parameters, such as mechanism of gene regulations at the module level. For that one, this paper explores methods to extract further information using the estimated parameters, that is, reconstruction of a module network from time-course gene expression data

PageMan: an interactive ontology tool to generate, display, and annotate overview graphs for profiling experiments.

BackgroundMicroarray technology has become a widely accepted and standardized tool in biology. The first microarray data analysis programs were developed to support pair-wise comparison. However, as microarray experiments have become more routine, large scale experiments have become more common, which investigate multiple time points or sets of mutants or transgenics. To extract biological information from such high-throughput expression data, it is necessary to develop efficient analytical platforms, which combine manually curated gene ontologies with efficient visualization and navigation tools. Currently, most tools focus on a few limited biological aspects, rather than offering a holistic, integrated analysis.ResultsHere we introduce PageMan, a multiplatform, user-friendly, and stand-alone software tool that annotates, investigates, and condenses high-throughput microarray data in the context of functional ontologies. It includes a GUI tool to transform different ontologies into a suitable format, enabling the user to compare and choose between different ontologies. It is equipped with several statistical modules for data analysis, including over-representation analysis and Wilcoxon statistical testing. Results are exported in a graphical format for direct use, or for further editing in graphics programs.PageMan provides a fast overview of single treatments, allows genome-level responses to be compared across several microarray experiments covering, for example, stress responses at multiple time points. This aids in searching for trait-specific changes in pathways using mutants or transgenics, analyzing development time-courses, and comparison between species. In a case study, we analyze the results of publicly available microarrays of multiple cold stress experiments using PageMan, and compare the results to a previously published meta-analysis.PageMan offers a complete user's guide, a web-based over-representation analysis as well as a tutorial, and is freely available at .ConclusionPageMan allows multiple microarray experiments to be efficiently condensed into a single page graphical display. The flexible interface allows data to be quickly and easily visualized, facilitating comparisons within experiments and to published experiments, thus enabling researchers to gain a rapid overview of the biological responses in the experiments.

A mathematical model of the treatment and survival of patients with high-grade brain tumours

More years of life per patient are lost as the result of primary brain tumours than any other form of cancer. The most aggressive of these is known as glioblastoma (GBM). The median survival time of patients with GBM is under 10 months and the outlook has hardly improved over the past 20 years. Generally, these tumours are remarkably resistant to radiotherapy and yet about 2–3% of all GBMs appear to be cured. The objectives of this study were to formulate a mathematical and phenomenological model of tumour growth in a population of patients with GBM to predict survival, and to use the model to extract biological information from clinical data. The model describes the growth of the tumour and the resulting damage to the normal brain using simple concepts borrowed from chemical reaction engineering. Death is assumed to result when the amount of surviving normal brain falls to a critical level. Radiotherapy is assumed to destroy tumour but not healthy brain. Simple rules are included to represent approximately the clinician's decisions about what type of treatment to offer each patient. A population of patients is constructed by assuming that key parameters can be sampled from statistical distributions. Following Monte Carlo simulation, the model can be fitted to data from clinical trials. The model reproduces clinical data extremely accurately. This suggests that the long-term survivors are not a separate sub-population but are the ‘lucky tail’ of a unimodal distribution. The estimated values of radiation sensitivity (represented as SF2, the survival fraction after 2 Gy) suggest the presence of severe hypoxia, which renders cells less sensitive to radiation. The model can predict the probable age distribution of tumours at presentation. The model shows the complicated effects of waiting times for treatment on the survival outcomes, and is used to predict the effects of escalation of radiotherapy dose. The model may aid the design of clinical trials using radiotherapy for patients with GBM, especially in helping to estimate the size of trial required. It is also designed in a generic form, and might be applicable to other tumour types.

Biological relation extraction and query answering from MEDLINE abstracts using ontology-based text mining

The rapid growth of the biological text data repository makes it difficult for human beings to access required information in a convenient and effective manner. The problem arises due to the fact that most of the information is embedded within unstructured or semi-structured text that computers cannot interpret very easily. In this paper we have presented an ontology-based Biological Information Extraction and Query Answering (BIEQA) System, which initiates text mining with a set of concepts stored in a biological ontology, and thereafter mines possible biological relations among those concepts using NLP techniques and co-occurrence-based analysis. The system extracts all frequently occurring biological relations among a pair of biological concepts through text mining. A mined relation is associated to a fuzzy membership value, which is proportional to its frequency of occurrence in the corpus and is termed a fuzzy biological relation. The fuzzy biological relations extracted from a text corpus along with other relevant information components like biological entities occurring within a relation, are stored in a database. The database is integrated with a query-processing module. The query-processing module has an interface, which guides users to formulate biological queries at different levels of specificity.

Microarray analysis of the transcriptome as a stepping stone towards understanding biological systems: practical considerations and perspectives

DNA microarrays have been used to characterize plant transcriptomes to answer various biological questions. While many studies have provided significant insights, there has been great debate about the general reliability of the technology and data analysis. When compared to well-established transcript analysis technologies, such as RNA blot analysis or quantitative reverse transcription-PCR, discrepancies have frequently been observed. The reasons for these discrepancies often relate to the technical and experimental systems. This review-tutorial addresses common problems in microarray analysis and describes: (i) methods to maximize extraction of valuable biological information from the vast amount of microarray data and (ii) approaches to balance resource availability with high scientific standards and technological innovation with peer acceptability.

Evaluation of BioCreAtIvE assessment of task 2

BackgroundMolecular Biology accumulated substantial amounts of data concerning functions of genes and proteins. Information relating to functional descriptions is generally extracted manually from textual data and stored in biological databases to build up annotations for large collections of gene products. Those annotation databases are crucial for the interpretation of large scale analysis approaches using bioinformatics or experimental techniques. Due to the growing accumulation of functional descriptions in biomedical literature the need for text mining tools to facilitate the extraction of such annotations is urgent. In order to make text mining tools useable in real world scenarios, for instance to assist database curators during annotation of protein function, comparisons and evaluations of different approaches on full text articles are needed.ResultsThe Critical Assessment for Information Extraction in Biology (BioCreAtIvE) contest consists of a community wide competition aiming to evaluate different strategies for text mining tools, as applied to biomedical literature. We report on task two which addressed the automatic extraction and assignment of Gene Ontology (GO) annotations of human proteins, using full text articles. The predictions of task 2 are based on triplets of protein – GO term – article passage. The annotation-relevant text passages were returned by the participants and evaluated by expert curators of the GO annotation (GOA) team at the European Institute of Bioinformatics (EBI). Each participant could submit up to three results for each sub-task comprising task 2. In total more than 15,000 individual results were provided by the participants. The curators evaluated in addition to the annotation itself, whether the protein and the GO term were correctly predicted and traceable through the submitted text fragment.ConclusionConcepts provided by GO are currently the most extended set of terms used for annotating gene products, thus they were explored to assess how effectively text mining tools are able to extract those annotations automatically. Although the obtained results are promising, they are still far from reaching the required performance demanded by real world applications. Among the principal difficulties encountered to address the proposed task, were the complex nature of the GO terms and protein names (the large range of variants which are used to express proteins and especially GO terms in free text), and the lack of a standard training set. A range of very different strategies were used to tackle this task. The dataset generated in line with the BioCreative challenge is publicly available and will allow new possibilities for training information extraction methods in the domain of molecular biology.

Overview of BioCreAtIvE: critical assessment of information extraction for biology

BackgroundThe goal of the first BioCreAtIvE challenge (Critical Assessment of Information Extraction in Biology) was to provide a set of common evaluation tasks to assess the state of the art for text mining applied to biological problems. The results were presented in a workshop held in Granada, Spain March 28–31, 2004. The articles collected in this BMC Bioinformatics supplement entitled "A critical assessment of text mining methods in molecular biology" describe the BioCreAtIvE tasks, systems, results and their independent evaluation.ResultsBioCreAtIvE focused on two tasks. The first dealt with extraction of gene or protein names from text, and their mapping into standardized gene identifiers for three model organism databases (fly, mouse, yeast). The second task addressed issues of functional annotation, requiring systems to identify specific text passages that supported Gene Ontology annotations for specific proteins, given full text articles.ConclusionThe first BioCreAtIvE assessment achieved a high level of international participation (27 groups from 10 countries). The assessment provided state-of-the-art performance results for a basic task (gene name finding and normalization), where the best systems achieved a balanced 80% precision / recall or better, which potentially makes them suitable for real applications in biology. The results for the advanced task (functional annotation from free text) were significantly lower, demonstrating the current limitations of text-mining approaches where knowledge extrapolation and interpretation are required. In addition, an important contribution of BioCreAtIvE has been the creation and release of training and test data sets for both tasks. There are 22 articles in this special issue, including six that provide analyses of results or data quality for the data sets, including a novel inter-annotator consistency assessment for the test set used in task 2.

BioCreAtIvE Task 1A: gene mention finding evaluation

BackgroundThe biological research literature is a major repository of knowledge. As the amount of literature increases, it will get harder to find the information of interest on a particular topic. There has been an increasing amount of work on text mining this literature, but comparing this work is hard because of a lack of standards for making comparisons. To address this, we worked with colleagues at the Protein Design Group, CNB-CSIC, Madrid to develop BioCreAtIvE (Critical Assessment for Information Extraction in Biology), an open common evaluation of systems on a number of biological text mining tasks. We report here on task 1A, which deals with finding mentions of genes and related entities in text. "Finding mentions" is a basic task, which can be used as a building block for other text mining tasks. The task makes use of data and evaluation software provided by the (US) National Center for Biotechnology Information (NCBI).Results15 teams took part in task 1A. A number of teams achieved scores over 80% F-measure (balanced precision and recall). The teams that tried to use their task 1A systems to help on other BioCreAtIvE tasks reported mixed results.ConclusionThe 80% plus F-measure results are good, but still somewhat lag the best scores achieved in some other domains such as newswire, due in part to the complexity and length of gene names, compared to person or organization names in newswire.

An architecture for biological information extraction and representation

Technological advances in biomedical research are generating a plethora of heterogeneous data at a high rate. There is a critical need for extraction, integration and management tools for information discovery and synthesis from these heterogeneous data. In this paper, we present a general architecture, called ALFA, for information extraction and representation from diverse biological data. The ALFA architecture consists of: (i) a networked, hierarchical, hyper-graph object model for representing information from heterogeneous data sources in a standardized, structured format; and (ii) a suite of integrated, interactive software tools for information extraction and representation from diverse biological data sources. As part of our research efforts to explore this space, we have currently prototyped the ALFA object model and a set of interactive software tools for searching, filtering, and extracting information from scientific text. In particular, we describe BioFerret, a meta-search tool for searching and filtering relevant information from the web, and ALFA Text Viewer, an interactive tool for user-guided extraction, disambiguation, and representation of information from scientific text. We further demonstrate the potential of our tools in integrating the extracted information with experimental data and diagrammatic biological models via the common underlying ALFA representation. aditya_vailaya@agilent.com.