An Integrated Approach for Characterizing Aerosol Climate Impacts and Environmental Interactions

Aerosols exert myriad influences on the earth's environment and climate, and on human health. The complexity of aerosol-related processes requires that information gathered to improve our understanding of climate change must originate from multiple sources, and that effective strategies for data integration need to be established. While a vast array of observed and modeled data are becoming available, the aerosol research community currently lacks the necessary tools and infrastructure to reap maximum scientific benefit from these data. Spatial and temporal sampling differences among a diverse set of sensors, nonuniform data qualities, aerosol mesoscale variabilities, and difficulties in separating cloud effects are some of the challenges that need to be addressed. Maximizing the long-term benefit from these data also requires maintaining consistently well-understood accuracies as measurement approaches evolve and improve. Achieving a comprehensive understanding of how aerosol physical, chemical, and radiative processes impact the earth system can be achieved only through a multidisciplinary, inter-agency, and international initiative capable of dealing with these issues. A systematic approach, capitalizing on modern measurement and modeling techniques, geospatial statistics methodologies, and high-performance information technologies, can provide the necessary machinery to support this objective. We outline a framework for integrating and interpreting observations and models, and establishing an accurate, consistent, and cohesive long-term record, following a strategy whereby information and tools of progressively greater sophistication are incorporated as problems of increasing complexity are tackled. This concept is named the Progressive Aerosol Retrieval and Assimilation Global Observing Network (PARAGON). To encompass the breadth of the effort required, we present a set of recommendations dealing with data interoperability; measurement and model integration; multisensor synergy; data summarization and mining; model evaluation; calibration and validation; augmentation of surface and in situ measurements; advances in passive and active remote sensing; and design of satellite missions. Without an initiative of this nature, the scientific and policy communities will continue to struggle with understanding the quantitative impact of complex aerosol processes on regional and global climate change and air quality

OpenDMAP: An open source, ontology-driven concept analysis engine, with applications to capturing knowledge regarding protein transport, protein interactions and cell-type-specific gene expression

<p>Abstract</p> <p>Background</p> <p>Information extraction (IE) efforts are widely acknowledged to be important in harnessing the rapid advance of biomedical knowledge, particularly in areas where important factual information is published in a diverse literature. Here we report on the design, implementation and several evaluations of OpenDMAP, an ontology-driven, integrated concept analysis system. It significantly advances the state of the art in information extraction by leveraging knowledge in ontological resources, integrating diverse text processing applications, and using an expanded pattern language that allows the mixing of syntactic and semantic elements and variable ordering.</p> <p>Results</p> <p>OpenDMAP information extraction systems were produced for extracting protein transport assertions (transport), protein-protein interaction assertions (interaction) and assertions that a gene is expressed in a cell type (expression). Evaluations were performed on each system, resulting in F-scores ranging from .26 – .72 (precision .39 – .85, recall .16 – .85). Additionally, each of these systems was run over all abstracts in MEDLINE, producing a total of 72,460 transport instances, 265,795 interaction instances and 176,153 expression instances. </p> <p>Conclusion</p> <p>OpenDMAP advances the performance standards for extracting protein-protein interaction predications from the full texts of biomedical research articles. Furthermore, this level of performance appears to generalize to other information extraction tasks, including extracting information about predicates of more than two arguments. The output of the information extraction system is always constructed from elements of an ontology, ensuring that the knowledge representation is grounded with respect to a carefully constructed model of reality. The results of these efforts can be used to increase the efficiency of manual curation efforts and to provide additional features in systems that integrate multiple sources for information extraction. The open source OpenDMAP code library is freely available at <url>http://bionlp.sourceforge.net/</url></p

Corpus Design For Biomedical Natural Language Processing

This paper classifies six publicly available biomedical corpora according to various corpus design features and characteristics

Pacific Symposium on Biocomputing 10:174-185(2005) IMPLICATIONS OF COMPOSITIONALITY IN THE GENE ONTOLOGY FOR ITS CURATION AND USAGE

In this paper we argue that a richer underlying representational model for the Gene Ontology that captures the implicit compositional structure of GO terms could have a positive impact on two activities crucial to the success of GO: ontology curation and database annotation. We show that many of the new terms added to GO in a one-year span appear to be compositional variations of other terms. We found that 90.2 % of the 3,652 new terms added between July 2003 and July 2004 exhibited characteristics of compositionality. We also examine annotations available from the GO Consortium website that are either manually curated or automatically generated. We found that 74.5 % and 63.2 % of GO terms are seldom, if ever, used in manual and automatic annotations, respectively. We show that there are features that tend to distinguish terms that are used from those that are not. In order to characterize the effect of compositionality on the combinatorial properties of GO, we employ finite state automata that represent sets of GO terms. This representational tool demonstrates how ontologies can grow very fast, and also shows that small conceptual changes can directly result in a large number of changes to the terminology. We argue that the curation and annotation findings we report are influenced by the combinatorial properties that present themselves in an ontology that does not have a model that properly captures the compositional structure of its terms. 1

BioCreAtIvE Task1A: entity identification with a stochastic tagger

Abstract Background Our approach to Task 1A was inspired by Tanabe and Wilbur's ABGene system 12. Like Tanabe and Wilbur, we approached the problem as one of part-of-speech tagging, adding a GENE tag to the standard tag set. Where their system uses the Brill tagger, we used TnT, the Trigrams 'n' Tags HMM-based part-of-speech tagger 3. Based on careful error analysis, we implemented a set of post-processing rules to correct both false positives and false negatives. We participated in both the open and the closed divisions; for the open division, we made use of data from NCBI. Results Our base system without post-processing achieved a precision and recall of 68.0% and 77.2%, respectively, giving an F-measure of 72.3%. The full system with post-processing achieved a precision and recall of 80.3% and 80.5% giving an F-measure of 80.4%. We achieved a slight improvement (F-measure = 80.9%) by employing a dictionary-based post-processing step for the open division. We placed third in both the open and the closed division. Conclusion Our results show that a part-of-speech tagger can be augmented with post-processing rules resulting in an entity identification system that competes well with other approaches.</p

Figure 2A shows the effect of term length for the cross validation data

<p><b>Copyright information:</b></p><p>Taken from "BioCreAtIvE Task1A: entity identification with a stochastic tagger"</p><p></p><p>BMC Bioinformatics 2005;6(Suppl 1):S4-S4.</p><p>Published online 24 May 2005</p><p>PMCID:PMC1869018.</p><p></p> Figure 2B shows the effect of term length for the official test data