http://www.dspace.cam.ac.uk:80/handle/1810/227506 Fri, 24 May 2013 18:36:58 GMT 2013-05-24T18:36:58Z http://www.dspace.cam.ac.uk:80/handle/1810/243406 Title: Biomedical event extraction from abstracts and full papers using search-based structured prediction Authors: Vlachos, Andreas; Craven, Mark Abstract: Abstract Background Biomedical event extraction has attracted substantial attention as it can assist researchers in understanding the plethora of interactions among genes that are described in publications in molecular biology. While most recent work has focused on abstracts, the BioNLP 2011 shared task evaluated the submitted systems on both abstracts and full papers. In this article, we describe our submission to the shared task which decomposes event extraction into a set of classification tasks that can be learned either independently or jointly using the search-based structured prediction framework. Our intention is to explore how these two learning paradigms compare in the context of the shared task. Results We report that models learned using search-based structured prediction exceed the accuracy of independently learned classifiers by 8.3 points in F-score, with the gains being more pronounced on the more complex Regulation events (13.23 points). Furthermore, we show how the trade-off between recall and precision can be adjusted in both learning paradigms and that search-based structured prediction achieves better recall at all precision points. Finally, we report on experiments with a simple domain-adaptation method, resulting in the second-best performance achieved by a single system. Conclusions We demonstrate that joint inference using the search-based structured prediction framework can achieve better performance than independently learned classifiers, thus demonstrating the potential of this learning paradigm for event extraction and other similarly complex information-extraction tasks. Description: RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are. Mon, 25 Jun 2012 23:00:00 GMT http://www.dspace.cam.ac.uk:80/handle/1810/243406 2012-06-25T23:00:00Z http://www.dspace.cam.ac.uk:80/handle/1810/243359 Title: Examples of plotter output, screenshots, and photographs of usage from Ivan Sutherland's Sketchpad program on the TX2 computer. Authors: Sutherland, Ivan Tue, 01 Jan 1963 00:00:00 GMT http://www.dspace.cam.ac.uk:80/handle/1810/243359 1963-01-01T00:00:00Z http://www.dspace.cam.ac.uk:80/handle/1810/240741 Title: Trends in modeling Biomedical Complex Systems Abstract: Abstract In this paper we provide an introduction to the techniques for multi-scale complex biological systems, from the single bio-molecule to the cell, combining theoretical modeling, experiments, informatics tools and technologies suitable for biological and biomedical research, which are becoming increasingly multidisciplinary, multidimensional and information-driven. The most important concepts on mathematical modeling methodologies and statistical inference, bioinformatics and standards tools to investigate complex biomedical systems are discussed and the prominent literature useful to both the practitioner and the theoretician are presented. Description: RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are. Wed, 14 Oct 2009 23:00:00 GMT http://www.dspace.cam.ac.uk:80/handle/1810/240741 2009-10-14T23:00:00Z http://www.dspace.cam.ac.uk:80/handle/1810/240491 Title: Minimizing Detection Probability Routing in Ad Hoc Networks Using Directional Antennas Abstract: In a hostile environment, it is important for a transmitter to make its wireless transmission invisible to adversaries because an adversary can detect the transmitter if the received power at its antennas is strong enough. This paper defines a detection probability model to compute the level of a transmitter being detected by a detection system at arbitrary location around the transmitter. Our study proves that the probability of detecting a directional antenna is much lower than that of detecting an omnidirectional antenna if both the directional and omnidirectional antennas provide the same Effective Isotropic Radiated Power (EIRP) in the direction of the receiver. We propose a Minimizing Detection Probability (MinDP) routing algorithm to find a secure routing path in ad hoc networks where nodes employ directional antennas to transmit data to decrease the probability of being detected by adversaries. Our study shows that the MinDP routing algorithm can reduce the total detection probability of deliveries from the source to the destination by over 74%. Description: RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are. Sun, 07 Jun 2009 23:00:00 GMT http://www.dspace.cam.ac.uk:80/handle/1810/240491 2009-06-07T23:00:00Z http://www.dspace.cam.ac.uk:80/handle/1810/238287 Title: Exploring subdomain variation in biomedical language Authors: Lippincott, Thomas; Ó Séaghdha, Diarmuid; Korhonen, Anna Abstract: AbstractApplications of Natural Language Processing (NLP) technology to biomedical texts have generated significant interest in recent years. In this paper we identify and investigate the phenomenon of linguistic subdomain variation within the biomedical domain, i.e., the extent to which different subject areas of biomedicine are characterised by different linguistic behaviour. While variation at a coarser domain level such as between newswire and biomedical text is well-studied and known to affect the portability of NLP systems, we are the first to conduct an extensive investigation into more fine-grained levels of variation. Using the large OpenPMC text corpus, which spans the many subdomains of biomedicine, we investigate variation across a number of lexical, syntactic, semantic and discourse-related dimensions. These dimensions are chosen for their relevance to the performance of NLP systems. We use clustering techniques to analyse commonalities and distinctions among the subdomains. We find that while patterns of inter-subdomain variation differ somewhat from one feature set to another, robust clusters can be identified that correspond to intuitive distinctions such as that between clinical and laboratory subjects. In particular, subdomains relating to genetics and molecular biology, which are the most common sources of material for training and evaluating biomedical NLP tools, are not representative of all biomedical subdomains. We conclude that an awareness of subdomain variation is important when considering the practical use of language processing applications by biomedical researchers. Thu, 26 May 2011 23:00:00 GMT http://www.dspace.cam.ac.uk:80/handle/1810/238287 2011-05-26T23:00:00Z http://www.dspace.cam.ac.uk:80/handle/1810/238020 Title: The role of gene fusions in the evolution of metabolic pathways: the histidine biosynthesis case Abstract: Abstract Background Histidine biosynthesis is one of the best characterized anabolic pathways. There is a large body of genetic and biochemical information available, including operon structure, gene expression, and increasingly larger sequence databases. For over forty years this pathway has been the subject of extensive studies, mainly in Escherichia coli and Salmonella enterica, in both of which details of histidine biosynthesis appear to be identical. In these two enterobacteria the pathway is unbranched, includes a number of unusual reactions, and consists of nine intermediates; his genes are arranged in a compact operon (hisGDC [NB]HAF [IE]), with three of them (hisNB, hisD and hisIE) coding for bifunctional enzymes. We performed a detailed analysis of his gene fusions in available genomes to understand the role of gene fusions in shaping this pathway. Results The analysis of HisA structures revealed that several gene elongation events are at the root of this protein family: internal duplication have been identified by structural superposition of the modules composing the TIM-barrel protein. Several his gene fusions happened in distinct taxonomic lineages; hisNB originated within γ-proteobacteria and after its appearance it was transferred to Campylobacter species (ε-proteobacteria) and to some Bacteria belonging to the CFB group. The transfer involved the entire his operon. The hisIE gene fusion was found in several taxonomic lineages and our results suggest that it probably happened several times in distinct lineages. Gene fusions involving hisIE and hisD genes (HIS4) and hisH and hisF genes (HIS7) took place in the Eukarya domain; the latter has been transferred to some δ-proteobacteria. Conclusion Gene duplication is the most widely known mechanism responsible for the origin and evolution of metabolic pathways; however, several other mechanisms might concur in the process of pathway assembly and gene fusion appeared to be one of the most important and common. Description: RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are. Wed, 15 Aug 2007 23:00:00 GMT http://www.dspace.cam.ac.uk:80/handle/1810/238020 2007-08-15T23:00:00Z http://www.dspace.cam.ac.uk:80/handle/1810/238019 Title: Modeling HIV quasispecies evolutionary dynamics Abstract: Abstract Background During the HIV infection several quasispecies of the virus arise, which are able to use different coreceptors, in particular the CCR5 and CXCR4 coreceptors (R5 and X4 phenotypes, respectively). The switch in coreceptor usage has been correlated with a faster progression of the disease to the AIDS phase. As several pharmaceutical companies are starting large phase III trials for R5 and X4 drugs, models are needed to predict the co-evolutionary and competitive dynamics of virus strains. Results We present a model of HIV early infection which describes the dynamics of R5 quasispecies and a model of HIV late infection which describes the R5 to X4 switch. We report the following findings: after superinfection (multiple infections at different times) or coinfection (simultaneous infection by different strains), quasispecies dynamics has time scales of several months and becomes even slower at low number of CD4+ T cells. Phylogenetic inference of chemokine receptors suggests that viral mutational pathway may generate a large variety of R5 variants able to interact with chemokine receptors different from CXCR4. The decrease of CD4+ T cells, during AIDS late stage, can be described taking into account the X4-related Tumor Necrosis Factor dynamics. Conclusion The results of this study bridge the gap between the within-patient and the inter-patients (i.e. world-wide) evolutionary processes during HIV infection and may represent a framework relevant for modeling vaccination and therapy. Description: RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are. Wed, 15 Aug 2007 23:00:00 GMT http://www.dspace.cam.ac.uk:80/handle/1810/238019 2007-08-15T23:00:00Z http://www.dspace.cam.ac.uk:80/handle/1810/237931 Title: Analysis of plasmid genes by phylogenetic profiling and visualization of homology relationships using Blast2Network Authors: Brilli, Matteo; Mengoni, Alessio; Fondi, Marco; Bazzicalupo, Marco; Lio, Pietro; Fani, Renato Abstract: Abstract Background Phylogenetic methods are well-established bioinformatic tools for sequence analysis, allowing to describe the non-independencies of sequences because of their common ancestor. However, the evolutionary profiles of bacterial genes are often complicated by hidden paralogy and extensive and/or (multiple) horizontal gene transfer (HGT) events which make bifurcating trees often inappropriate. In this context, plasmid sequences are paradigms of network-like relationships characterizing the evolution of prokaryotes. Actually, they can be transferred among different organisms allowing the dissemination of novel functions, thus playing a pivotal role in prokaryotic evolution. However, the study of their evolutionary dynamics is complicated by the absence of universally shared genes, a prerequisite for phylogenetic analyses. Results To overcome such limitations we developed a bioinformatic package, named Blast2Network (B2N), allowing the automatic phylogenetic profiling and the visualization of homology relationships in a large number of plasmid sequences. The software was applied to the study of 47 completely sequenced plasmids coming from Escherichia, Salmonella and Shigella spps. Conclusion The tools implemented by B2N allow to describe and visualize in a new way some of the evolutionary features of plasmid molecules of Enterobacteriaceae; in particular it helped to shed some light on the complex history of Escherichia, Salmonella and Shigella plasmids and to focus on possible roles of unannotated proteins. The proposed methodology is general enough to be used for comparative genomic analyses of bacteria. Description: RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are. Sun, 21 Dec 2008 00:00:00 GMT http://www.dspace.cam.ac.uk:80/handle/1810/237931 2008-12-21T00:00:00Z http://www.dspace.cam.ac.uk:80/handle/1810/237904 Title: The first step in the development of text mining technology for cancer risk assessment: identifying and organizing scientific evidence in risk assessment literature Authors: Korhonen, Anna; Silins, Ilona; Sun, Lin; Stenius, Ulla Abstract: Abstract Background One of the most neglected areas of biomedical Text Mining (TM) is the development of systems based on carefully assessed user needs. We have recently investigated the user needs of an important task yet to be tackled by TM -- Cancer Risk Assessment (CRA). Here we take the first step towards the development of TM technology for the task: identifying and organizing the scientific evidence required for CRA in a taxonomy which is capable of supporting extensive data gathering from biomedical literature. Results The taxonomy is based on expert annotation of 1297 abstracts downloaded from relevant PubMed journals. It classifies 1742 unique keywords found in the corpus to 48 classes which specify core evidence required for CRA. We report promising results with inter-annotator agreement tests and automatic classification of PubMed abstracts to taxonomy classes. A simple user test is also reported in a near real-world CRA scenario which demonstrates along with other evaluation that the resources we have built are well-defined, accurate, and applicable in practice. Conclusion We present our annotation guidelines and a tool which we have designed for expert annotation of PubMed abstracts. A corpus annotated for keywords and document relevance is also presented, along with the taxonomy which organizes the keywords into classes defining core evidence for CRA. As demonstrated by the evaluation, the materials we have constructed provide a good basis for classification of CRA literature along multiple dimensions. They can support current manual CRA as well as facilitate the development of an approach based on TM. We discuss extending the taxonomy further via manual and machine learning approaches and the subsequent steps required to develop TM technology for the needs of CRA. Description: RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are. Mon, 21 Sep 2009 23:00:00 GMT http://www.dspace.cam.ac.uk:80/handle/1810/237904 2009-09-21T23:00:00Z http://www.dspace.cam.ac.uk:80/handle/1810/237888 Title: Measuring similarity between gene expression profiles: a Bayesian approach Abstract: Abstract Background Grouping genes into clusters on the basis of similarity between their expression profiles has been the main approach to predict functional modules, from which important inference or further investigation decision could be made. While the univocal determination of similarity metric is important, current practices are normally involved with Euclidean distance and Pearson correlation, of which assumptions are not likely the case for high-throughput microarray data. Results We advocate the use of a novel metric - BayesGen - to measure similarity between gene expression profiles, and demonstrate its performance on two important applications: constructing genome-wide co-expression network, and clustering cancer human tissues into subtypes. BayesGen is formulated as the evidence ratio between two alternative hypotheses about the generating mechanism of a given pair of genes, and incorporates as prior knowledge the global characteristics of the whole dataset. Through the joint modelling of expected intensity levels and noise variances, it addresses the inherent nonlinearity and the association of noise levels across different microarray value ranges. The full Bayesian formulation also facilitates the possibility of meta-analysis. Conclusion BayesGen allows more effective extraction of similarity information between genes from microarray expression data, which has significant effect on various inference tasks. It also provides a robust choice for other object-feature data, as illustrated through the results of the test on synthetic data. Description: RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are. Thu, 03 Dec 2009 00:00:00 GMT http://www.dspace.cam.ac.uk:80/handle/1810/237888 2009-12-03T00:00:00Z http://www.dspace.cam.ac.uk:80/handle/1810/237878 Title: Formal reasoning on qualitative models of coinfection of HIV and Tuberculosis and HAART therapy Abstract: Abstract Background Several diseases, many of which nowadays pandemic, consist of multifactorial pathologies. Paradigmatic examples come from the immune response to pathogens, in which cases the effects of different infections combine together, yielding complex mutual feedback, often a positive one that boosts infection progression in a scenario that can easily become lethal. HIV is one such infection, which weakens the immune system favouring the insurgence of opportunistic infections, amongst which Tuberculosis (TB). The treatment with antiretroviral therapies has shown effective in reducing mortality. An in-depth understanding of complex systems, like the one consisting of HIV, TB and related therapies, is an open great challenge, on the boundaries of bioinformatics, computational and systems biology. Results We present a simplified formalisation of the highly dynamic system consisting of HIV, TB and related therapies, at the cellular level. The progression of the disease (AIDS) depends hence on interactions between viruses, cells, chemokines, the high mutation rate of viruses, the immune response of individuals and the interaction between drugs and infection dynamics. We first discuss a deterministic model of dual infection (HIV and TB) which is able to capture the long-term dynamics of CD4 T cells, viruses and Tumour Necrosis Factor (TNF). We contrast this model with a stochastic approach which captures intrinsic fluctuations of the biological processes. Furthermore, we also integrate automated reasoning techniques, i.e. probabilistic model checking, in our formal analysis. Beyond numerical simulations, model checking allows general properties (effectiveness of anti-HIV therapies) to be verified against the models by means of an automated procedure. Our work stresses the growing importance and flexibility of model checking techniques in bioinformatics. In this paper we i) describe HIV as a complex case of infectious diseases; ii) provide a number of different formal descriptions that suitably account for aspects of interests; iii) suggest that the integration of different models together with automated reasoning techniques can improve the understanding of infections and therapies through formal analysis methodologies. Conclusion We argue that the described methodology suitably supports the study of viral infections in a formal, automated and expressive manner. We envisage a long-term contribution of this kind of approaches to clinical Bioinformatics and Translational Medicine. Description: RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are. Mon, 18 Jan 2010 00:00:00 GMT http://www.dspace.cam.ac.uk:80/handle/1810/237878 2010-01-18T00:00:00Z http://www.dspace.cam.ac.uk:80/handle/1810/237756 Title: A comparison and user-based evaluation of models of textual information structure in the context of cancer risk assessment Authors: Guo, Yufan; Korhonen, Anna; Liakata, Maria; Silins, Ilona; Hogberg, Johan; Stenius, Ulla Abstract: Abstract Background Many practical tasks in biomedicine require accessing specific types of information in scientific literature; e.g. information about the results or conclusions of the study in question. Several schemes have been developed to characterize such information in scientific journal articles. For example, a simple section-based scheme assigns individual sentences in abstracts under sections such as Objective, Methods, Results and Conclusions. Some schemes of textual information structure have proved useful for biomedical text mining (BIO-TM) tasks (e.g. automatic summarization). However, user-centered evaluation in the context of real-life tasks has been lacking. Methods We take three schemes of different type and granularity - those based on section names, Argumentative Zones (AZ) and Core Scientific Concepts (CoreSC) - and evaluate their usefulness for a real-life task which focuses on biomedical abstracts: Cancer Risk Assessment (CRA). We annotate a corpus of CRA abstracts according to each scheme, develop classifiers for automatic identification of the schemes in abstracts, and evaluate both the manual and automatic classifications directly as well as in the context of CRA. Results Our results show that for each scheme, the majority of categories appear in abstracts, although two of the schemes (AZ and CoreSC) were developed originally for full journal articles. All the schemes can be identified in abstracts relatively reliably using machine learning. Moreover, when cancer risk assessors are presented with scheme annotated abstracts, they find relevant information significantly faster than when presented with unannotated abstracts, even when the annotations are produced using an automatic classifier. Interestingly, in this user-based evaluation the coarse-grained scheme based on section names proved nearly as useful for CRA as the finest-grained CoreSC scheme. Conclusions We have shown that existing schemes aimed at capturing information structure of scientific documents can be applied to biomedical abstracts and can be identified in them automatically with an accuracy which is high enough to benefit a real-life task in biomedicine. Description: RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are. Tue, 08 Mar 2011 00:00:00 GMT http://www.dspace.cam.ac.uk:80/handle/1810/237756 2011-03-08T00:00:00Z