@article {Dietze2009GoWeb,
	title = {GoWeb: a semantic search engine for the life science web.},
	journal = {BMC bioinformatics},
	volume = {10 Suppl 10},
	number = {Suppl 10},
	year = {2009},
	pages = {S7+},
	abstract = {BACKGROUND: Current search engines are keyword-based. Semantic technologies promise a next generation of semantic search engines, which will be able to answer questions. Current approaches either apply natural language processing to unstructured text or they assume the existence of structured statements over which they can reason. RESULTS: Here, we introduce a third approach, GoWeb, which combines classical keyword-based Web search with text-mining and ontologies to navigate large results sets and facilitate question answering. We evaluate GoWeb on three benchmarks of questions on genes and functions, on symptoms and diseases, and on proteins and diseases. The first benchmark is based on the BioCreAtivE 1 Task 2 and links 457 gene names with 1352 functions. GoWeb finds 58\% of the functional GeneOntology annotations. The second benchmark is based on 26 case reports and links symptoms with diseases. GoWeb achieves 77\% success rate improving an existing approach by nearly 20\%. The third benchmark is based on 28 questions in the TREC genomics challenge and links proteins to diseases. GoWeb achieves a success rate of 79\%. CONCLUSION: GoWeb{\textquoteright}s combination of classical Web search with text-mining and ontologies is a first step towards answering questions in the biomedical domain. GoWeb is online at: http://www.gopubmed.org/goweb.},
	keywords = {it, ontologies, semantic-web},
	issn = {1471-2105},
	doi = {10.1186/1471-2105-10-S10-S7},
	url = {http://dx.doi.org/10.1186/1471-2105-10-S10-S7},
	author = {Dietze, Heiko and Schroeder, Michael}
}
@article {Friedman2006BioOntology,
	title = {Bio-Ontology and text: bridging the modeling gap},
	journal = {Bioinformatics},
	volume = {22},
	number = {19},
	year = {2006},
	month = {October},
	pages = {2421{\textendash}2429},
	abstract = {Motivation: Natural language processing (NLP) techniques are increasingly being used in biology to automate the capture of new biological discoveries in text, which are being reported at a rapid rate. Yet, information represented in NLP data structures is classically very different from information organized with ontologies as found in model organisms or genetic databases. To facilitate the computational reuse and integration of information buried in unstructured text with that of genetic databases, we propose and evaluate a translational schema that represents a comprehensive set of phenotypic and genetic entities, as well as their closely related biomedical entities and relations as expressed in natural language. In addition, the schema connects different scales of biological information, and provides mappings from the textual information to existing ontologies, which are essential in biology for integration, organization, dissemination and knowledge management of heterogeneous phenotypic information. A common comprehensive representation for otherwise heterogeneous phenotypic and genetic datasets, such as the one proposed, is critical for advancing systems biology because it enables acquisition and reuse of unprecedented volumes of diverse types of knowledge and information from text. Results: A novel representational schema, PGschema, was developed that enables translation of phenotypic, genetic and their closely related information found in textual narratives to a well-defined data structure comprising phenotypic and genetic concepts from established ontologies along with modifiers and relationships. Evaluation for coverage of a selected set of entities showed that 90\% of the information could be represented (95\% confidence interval: 86-93\%; n = 268). Moreover, PGschema can be expressed automatically in an XML format using natural language techniques to process the text. To our knowledge, we are providing the first evaluation of a translational schema for NLP that contains declarative knowledge about genes and their associated biomedical data (e.g. phenotypes). Availability: http://zellig.cpmc.columbia.edu/PGschema Contact: Friedman@dbmi.columbia.edu or Lussier@uchicago.edu 10.1093/bioinformatics/btl405},
	keywords = {data-mining, it, ontologies, text-mining},
	doi = {10.1093/bioinformatics/btl405},
	url = {http://dx.doi.org/10.1093/bioinformatics/btl405},
	author = {Friedman, Carol and Borlawsky, Tara and Shagina, Lyudmila and Xing, H. Rosie and Lussier, Yves A.}
}
@article {Lampe2008Research,
	title = {Research between natural and cultural history information},
	journal = {Journal on Computing and Cultural Heritage},
	volume = {1},
	number = {1},
	year = {2008},
	month = {June},
	pages = {1{\textendash}22},
	keywords = {biodiversity, cidoc, it, natural-history, ontologies, semantic-web},
	issn = {15564673},
	doi = {10.1145/1367080.1367084},
	url = {http://dx.doi.org/10.1145/1367080.1367084},
	author = {Lampe, Karl-Heinz and Riede, Klaus and Doerr, Martin}
}
@article {delaCalle2009BIRI,
	title = {BIRI: a new approach for automatically discovering and indexing available public bioinformatics resources from the literature.},
	journal = {BMC bioinformatics},
	volume = {10},
	number = {1},
	year = {2009},
	month = {October},
	pages = {320+},
	abstract = {BACKGROUND: The rapid evolution of Internet technologies and the collaborative approaches that dominate the field have stimulated the development of numerous bioinformatics resources. To address this new framework, several initiatives have tried to organize these services and resources. In this paper, we present the BioInformatics Resource Inventory (BIRI), a new approach for automatically discovering and indexing available public bioinformatics resources using information extracted from the scientific literature. The index generated can be automatically updated by adding additional manuscripts describing new resources. We have developed web services and applications to test and validate our approach. It has not been designed to replace current indexes but to extend their capabilities with richer functionalities. RESULTS: We developed a web service to provide a set of high-level query primitives to access the index. The web service can be used by third-party web services or web-based applications. To test the web service, we created a pilot web application to access a preliminary knowledge base of resources. We tested our tool using an initial set of 400 abstracts. Almost 90\% of the resources described in the abstracts were correctly classified. More than 500 descriptions of functionalities were extracted. CONCLUSION: These experiments suggest the feasibility of our approach for automatically discovering and indexing current and future bioinformatics resources. Given the domain-independent characteristics of this tool, it is currently being applied by the authors in other areas, such as medical nanoinformatics. BIRI is available at http://edelman.dia.fi.upm.es/biri/.},
	keywords = {bioinformatics, data-mining, it, text-mining},
	issn = {1471-2105},
	doi = {10.1186/1471-2105-10-320},
	url = {http://dx.doi.org/10.1186/1471-2105-10-320},
	author = {de la Calle, Guillermo and Garc\'{\i}a-Remesal, Miguel and Chiesa, Stefano and de la Iglesia, Diana and Maojo, Victor}
}
@article {Dudley2009Quick,
	title = {A quick guide for developing effective bioinformatics programming skills.},
	journal = {PLoS computational biology},
	volume = {5},
	number = {12},
	year = {2009},
	month = {December},
	pages = {e1000589+},
	publisher = {Public Library of Science},
	keywords = {it, methods, programming},
	issn = {1553-7358},
	doi = {10.1371/journal.pcbi.1000589},
	url = {http://dx.doi.org/10.1371/journal.pcbi.1000589},
	author = {Dudley, Joel T. and Butte, Atul J.}
}
@article {Atkinson2007Exploratory,
	title = {Exploratory tools for clustering multivariate data},
	journal = {Computational Statistics \& Data Analysis},
	volume = {52},
	number = {1},
	year = {2007},
	month = {September},
	pages = {272{\textendash}285},
	abstract = {The forward search provides a series of robust parameter estimates based on increasing numbers of observations. The resulting series of robust Mahalanobis distances is used to cluster multivariate normal data. The method depends on envelopes of the distribution of the test statistics in forward plots. These envelopes can be found by simulation; flexible polynomial approximations to the envelopes are given. New graphical tools provide methods not only of detecting clusters but also of determining their membership. Comparisons are made with mclust and k -means clustering.},
	keywords = {algorithms, cluster, data-exploration, it},
	issn = {01679473},
	doi = {10.1016/j.csda.2006.12.034},
	url = {http://dx.doi.org/10.1016/j.csda.2006.12.034},
	author = {Atkinson, A. and Riani, M.}
}
@article {Mazarakis2007Vehicle,
	title = {Vehicle classification in Sensor Networks using time-domain signal processing and Neural Networks},
	journal = {Microprocessors and Microsystems},
	volume = {31},
	number = {6},
	year = {2007},
	month = {September},
	pages = {381{\textendash}392},
	abstract = {Vehicle classification is a demanding application of Wireless Sensor Networks. In many cases, sensor nodes detect and classify vehicles from their acoustic and/or seismic signature using spectral or wavelet based feature extraction methods. Such methods, while providing good results are quite demanding in computational power and energy and are difficult to implement on low-cost sensor nodes with limited resources. In this work, we investigate the use of a time-domain encoding and feature extraction method, to produce simple, fixed-size matrices from complex acoustic and seismic signatures of vehicles for classification purposes. Classification is accomplished using an Artificial Neural Network and a basic, L1 distance, archetype classifier. Hardware implementation issues on a prototype sensor node, based on an 8-bit microcontroller, are also discussed. For evaluation purposes we use real data from DARPA{\textquoteright}s SensIt project, which contains various acoustic and seismic signatures from two different vehicle types, a tracked vehicle and a heavy truck.},
	keywords = {asr, classification, it, neuralnetworks},
	issn = {01419331},
	doi = {10.1016/j.micpro.2007.02.005},
	url = {http://dx.doi.org/10.1016/j.micpro.2007.02.005},
	author = {Mazarakis, G. and Avaritsiotis, J.}
}
@article {Zeng2005Development,
	title = {Development of a noise sources classification system based on new method for feature selection},
	journal = {Applied Acoustics},
	volume = {66},
	number = {10},
	year = {2005},
	month = {October},
	pages = {1196{\textendash}1205},
	abstract = {Feature selection of noise sources is important for noise sources detection and classification. In this paper, a new rough set based feature selection method has been given. Based on the method, a noise sources automatic classification system (NSACS) has been designed and validated. The key idea of the method is that most effective features can distinguish the most number of samples belonging to different classes of noise sources, if they are used for classification. This new approach has been applied into the system NSACS to select relevant features for artificial datasets and real-world datasets and the results have shown that this approach can correctly select all the relevant features of artificial datasets and at the same time it can drastically reduce the number of features. From the experiments, it can be found that to consider all the five datasets, the number of classification features after selection drops to 35\% and the accurate classification rate increases about 14\%. For the underwater noise sources dataset the number of features drops to 1/5 and the accurate classification rate increases about 6\% after feature selection.},
	keywords = {asr, bioacoustics, classification, it},
	issn = {0003682X},
	doi = {10.1016/j.apacoust.2005.02.007},
	url = {http://dx.doi.org/10.1016/j.apacoust.2005.02.007},
	author = {Zeng, X. and Zhan, Y.}
}
@article {Cowling2003Comparison,
	title = {Comparison of techniques for environmental sound recognition},
	journal = {Pattern Recognition Letters},
	volume = {24},
	number = {15},
	year = {2003},
	month = {November},
	pages = {2895{\textendash}2907},
	abstract = {This paper presents a comprehensive comparative study of artificial neural networks, learning vector quantization and dynamic time warping classification techniques combined with stationary/non-stationary feature extraction for environmental sound recognition. Results show 70\% recognition using mel frequency cepstral coefficients or continuous wavelet transform with dynamic time warping.},
	keywords = {asr, environment, it, recognition, soundscapes},
	issn = {01678655},
	doi = {10.1016/S0167-8655(03)00147-8},
	url = {http://dx.doi.org/10.1016/S0167-8655(03)00147-8},
	author = {Cowling, M.}
}
@article {Kramer2004Outer,
	title = {Outer Bounds on the Capacity of Gaussian Interference Channels},
	journal = {IEEE Transactions on Information Theory},
	volume = {50},
	number = {3},
	year = {2004},
	month = {March},
	pages = {581{\textendash}586},
	abstract = {Two outer bounds on the capacity region of the two-user Gaussian interference channel (IFC) are derived. The idea of the first bound is to let a genie give each receiver just enough information to decode both messages. This bound unifies and improves the best known outer bounds of Sato and Carleial. Furthermore, the bound extends to discrete memoryless IFCs and is shown to be equivalent to another bound of Carleial. The second bound follows directly from existing results of Costa and Sato and possesses certain optimality properties for weak interference.},
	keywords = {communication, it, theory},
	issn = {0018-9448},
	doi = {10.1109/TIT.2004.825249},
	url = {http://dx.doi.org/10.1109/TIT.2004.825249},
	author = {Kramer, G.}
}
@article {Pettifer2009Active,
	title = {An active registry for bioinformatics web services.},
	journal = {Bioinformatics (Oxford, England)},
	volume = {25},
	number = {16},
	year = {2009},
	month = {August},
	pages = {2090{\textendash}2091},
	abstract = {SUMMARY: The EMBRACE Registry is a web portal that collects and monitors web services according to test scripts provided by the their administrators. Users are able to search for, rank and annotate services, enabling them to select the most appropriate working service for inclusion in their bioinformatics analysis tasks. AVAILABILITY AND IMPLEMENTATION: Web site implemented with PHP, Python, MySQL and Apache, with all major browsers supported. (www.embraceregistry.net).},
	keywords = {bioinformatics, it, web},
	issn = {1367-4811},
	doi = {10.1093/bioinformatics/btp329},
	url = {http://dx.doi.org/10.1093/bioinformatics/btp329},
	author = {Pettifer, S. and Thorne, D. and McDermott, P. and Attwood, T. and Baran, J. and Bryne, J. C. and Hupponen, T. and Mowbray, D. and Vriend, G.}
}
@article {Stein2008Towards,
	title = {Towards a cyberinfrastructure for the biological sciences: progress, visions and challenges},
	journal = {Nature Reviews Genetics},
	volume = {9},
	number = {9},
	year = {2008},
	month = {September},
	pages = {678{\textendash}688},
	publisher = {Nature Publishing Group},
	abstract = {Wiki pages and commentingBiology is an information-driven science. Large-scale data sets from genomics, physiology, population genetics and imaging are driving research at a dizzying rate. Simultaneously, interdisciplinary collaborations among experimental biologists, theorists, statisticians and computer scientists have become the key to making effective use of these data sets. However, too many biologists have trouble accessing and using these electronic data sets and tools effectively. A {\textquoteright}cyberinfrastructure{\textquoteright} is a combination of databases, network protocols and computational services that brings people, information and computational tools together to perform science in this information-driven world. This article reviews the components of a biological cyberinfrastructure, discusses current and pending implementations, and notes the many challenges that lie ahead.},
	keywords = {biodiversity20, collaboration, cyber, it},
	issn = {1471-0056},
	doi = {10.1038/nrg2414},
	url = {http://dx.doi.org/10.1038/nrg2414},
	author = {Stein, Lincoln D.}
}
