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Intelligent Systems, IEEE

Issue 6 • Date Nov-Dec 2001

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Displaying Results 1 - 17 of 17
  • Toward more intelligent annotation tools: a prototype

    Page(s): 42 - 51
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (185 KB)  

    Several research teams have addressed the problem of annotating sequence data computationally, but no genetic tools have emerged to help gather information on a given sequence or set of sequences. The authors present Precis, a prototype tool that automatically creates protein reports from concise information. View full abstract»

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  • Human language technologies for knowledge management

    Page(s): 84 - 94
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (417 KB)  

    Knowledge management has changed the way we look at knowledge in the current economy; it is a key factor in an enterprise's success or failure. In contrast to what we as engineers typically love, KM puts people first organizational issues second, and technology third. Seriously considering these issues to produce a successful KM system leads to at least three requirements. We must: encourage employees to participate; integrate KM with current organizational practice; and provide the natural tools such that people can easily recognize the benefits, align with current organizational practices, and use the system. The natural choice of substance for such a KM system is human language, and the required tools are based on human language understanding. As we all know, comprehensive human language understanding is out of reach for the foreseeable future. Nevertheless, although the knowledge system's substance is language and complete human language understanding is out of reach, the system need not be restricted to text nor the tools restricted to a keyword-based search. Human language technology can and should do better. The article elaborates on the interaction between human language technology and KM to achieve this goal. View full abstract»

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  • Intelligent systems in biology: why the excitement?

    Page(s): 8 - 13
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    Biology has rapidly become a data-rich, information-hungry science because of recent massive data generation technologies. Our biological colleagues are designing more clever and informative experiments because of recent advances in molecular science. These experiments and data hold the key to the deepest secrets of biology and medicine, but we cannot fully analyze this data due to the wealth and complexity of the information available. The result is a great need for intelligent systems in biology. There are many opportunities for intelligent systems to help produce knowledge in biology and medicine. Intelligent systems probably helped design the last drug your doctor prescribed, and they were probably involved in some aspect of the last medical care you received. Intelligent computational analysis of the human genome will drive medicine for at least the next half-century. Intelligent systems are working on gene expression data to help understand genetic regulation and ultimately the regulated control of all life processes including cancer, regeneration, and aging. Knowledge bases of metabolic pathways and other biological networks make inferences in systems biology that, for example, let a pharmaceutical program target a pathogen pathway that does not exist in humans, resulting in fewer side effects to patients. Modern intelligent analysis of biological sequences produces the most accurate picture of evolution ever achieved. Knowledge-based empirical approaches currently are the most successful method known for general protein structure prediction. Intelligent literature-access systems exploit a knowledge flow exceeding half a million biomedical articles per year. Machine learning systems exploit heterogenous online databases whose exponential growth mimics Moore's law. View full abstract»

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  • Challenges for intelligent systems in biology

    Page(s): 14 - 18
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    Biological processes have produced the ultimate intelligent system (humans), and now we are trying to understand biology (and ourselves) by building intelligent systems. Intelligent systems research in biology strives to understand how living systems perform difficult tasks routinely (ranging from molecular phenomena such as protein-folding to organism-level phenomena such as cognition). The definition of intelligent systems in biology can lead to hours of debate. Some say that all high-performance systems that do something difficult with (or to) biological data should be considered intelligent systems. Others insist that the term intelligent system should be reserved for systems using the methods typically associated with modem AI. View full abstract»

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  • Crazy clocks: counterintuitive consequences of "intelligent" automation

    Page(s): 74 - 76
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    Human error is a widely recognized problem, and there are at least two complementary paths to error mitigation. One approach aims to reduce error by changing the design of systems and products to make them fit human capabilities and limitations. Another approach aims to remove human error (and human involvement) altogether by automation, sometimes including intelligent systems. The latter approach might seem preferable. After all, if no human is involved, how can there be any human error? Both paths have merit. However, the automatization path might be so tempting that researchers might not realize the new, counterintuitive problems this approach can create. Recent attempts to eliminate human error in programming a VCR provide a poignant example of this concern. View full abstract»

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  • Diagnosis systems in medicine with reusable knowledge components

    Page(s): 68 - 73
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (140 KB)  

    Constructing knowledge systems is viewed as a modeling activity for developing structured knowledge and reasoning models. To ensure well-formed models, the use of some knowledge engineering methodology is crucial. Additionally, reusing models can significantly reduce the time and costs of building a new application. Reusing knowledge components across different applications and domains can help acquire expert knowledge and accurately describe the reasoning process. In fact, current knowledge engineering research has taken major initiatives in the development of knowledge systems by reusing generic components, such as ontologies or problem-solving methods. The article shows how we developed a diagnosis-aid system by reusing and adapting genetic knowledge components for diagnosing eye emergencies. View full abstract»

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  • A knowledge base for integrated biological systems

    Page(s): 52 - 61
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    Describes the creation of a knowledge base using AROM, a knowledge representation system that permits fine grained description of biological components and how they interact to create an integrated system. View full abstract»

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  • Sensor-based pedestrian protection

    Page(s): 77 - 81
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (609 KB)  

    Pedestrian accidents represent the second-largest source of traffic-related injuries and fatalities, after accidents involving car passengers. Children are especially at risk. A complementary approach to accident prevention is to focus on sensor-based solutions, which let vehicles "look ahead" and detect pedestrians in their surroundings. The article investigates the state of the art in this domain, reviewing passive, video based approaches and approaches involving active sensors (radar and laser range finders). View full abstract»

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  • Improving objectivity and scalability in protein crystallization

    Page(s): 26 - 34
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    This article describes the application of image analysis techniques to protein crystallization experiment classification. By applying knowledge discovery techniques to the analysis results, we can extract important crystallographic knowledge. View full abstract»

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  • Using combinatory categorial grammar to extract biomedical information

    Page(s): 62 - 67
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (119 KB)  

    Extracting information from biology databases manually can be an overwhelming task. GenBank, the US National Institutes of Health database containing all publicly available DNA sequences, has more than 14 billion bases in 13 million genetic-sequence records. Medline, a literature database available through PubMed, has over 11 million journal citations. In a May 2001 search request for "cytokine" (regulatory proteins in the immune system), PubMed returned 296556 articles. Given the quantity and complexity of biomedical literature, demands for computational tools to extract specific information are increasing. The author reviews biomedical information extraction methods and presents research done by KAIST's natural language processing group on a system that shows encouraging performance using combinatory categorial grammar as a natural language grammar formalism. View full abstract»

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  • Automatic pattern embedding in protein structure models

    Page(s): 21 - 25
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (204 KB)  

    This article describes an implementation of a set of rules for automatically embedding a minimal functional sequence pattern in a structural hidden Markov model. The final product is a library of wide ranging fold models with encoded information about the functional families. View full abstract»

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  • Donald Michie: secrets of Colossus revealed

    Page(s): 82 - 83
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    In 1943, Donald Michie, Alan Turing, and Jack Good were poised at the crossroads of AI. World War II, Hitler, German U-boats, and wartime code-breaking set the scene. Together they would take walks in the English countryside talking about various approaches, conjectures, and arguments concerning what today we call AI. They formed an intellectual cabal with a shared obsession with thinking machines and particularly with machine learning as the only credible road to achieving such machines. The breaking of the Naval Enigma code, which Turing worked on, and the building of Colossus, the first large electronic valve computer, which Michie and Good worked on, changed the war dramatically. View full abstract»

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  • The Asia-Pacific regional perspective on bioinformatics

    Page(s): 19 - 61
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    The Asia-Pacific region spans the Asian and Australasian continents as well as the Pacific-rim countries. As such, the seeds of bioinformatics in this region have been sown as early as 1989 in India, followed by Japan and Australia in 1991. While bioinformatics research, service, and education have reached laudable heights in these countries as well as in Singapore, Taiwan, Korea, Malaysia, New Zealand and Russia, several other countries (Thailand, Indonesia, and the Philippines to name three) are making considerable progress. Following the success story of Japan, the status of bioinformatics in the Asia-Pacific region is presented. View full abstract»

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  • Neuron function: the mystery persists

    Page(s): 4 - 7
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    There are currently three primary models of how neurons function, each with its uses and variations, according to James McClelland, a professor of psychology and computer science at Carnegie Mellon University and codirector of the Center for Neural Basis of Cognition. The first and simplest is the integrate-and-fire model, which is based on the idea that the neuron adds and subtracts excitatory and inhibitory inputs until it reaches a threshold, at which point it fires a single impulse or action potential. Another model is the sigmoid transfer function, in which the neuron adds up excitatory and inhibitory inputs (as in the integrate-and-fire model) but treats the output as a continuous quantity. Finally, in the sigma-pi unit model, a neuron's output is equal to the sum of many products, each consisting of a multiplication of several inputs. View full abstract»

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  • The impact of European bioinformatics

    Page(s): 18 - 19
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    Europe is undergoing a political integration process, the goals of which are still under discussion. Some countries, such as Germany, France, Italy, Spain, Holland, and Belgium, have coordinated aspects of their economy, including a common currency, under the administration of the European Commission. Key countries such as the UK and Denmark are still undecided about their degree of participation, and others, including most of the former Eastern-block countries, are actively seeking integration. The EC has limited power in equilibrium with the European parliament and national and regional governments. Direct funding of the sciences was not included in the EC's mandate. It was considered a strategic responsibility of the national governments. This political concept evolved into new modes of collaboration between countries and the EC. Bioinformatics could be a key area for these developments, because, if properly coordinated, it has great potential for generating added value to the new genomics and proteomic technology. Currently, some bioinformatics-related research is funded as networks of groups from separate countries, and others are funded as basic research services, such as databases. For example, the E-Biosci project, coordinated by the European Molecular Biology Laboratory as a large research institution, includes the participation of groups from different countries and one or two medium-sized companies. Other computational science and telecommunications-related programs are rarely accessible to the bioinformatics community. View full abstract»

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  • Geno2pheno: interpreting genotypic HIV drug resistance tests

    Page(s): 35 - 41
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    This intelligent system uses information encoded in the HIV genomic sequence to predict the virus's resistance or susceptibility to drugs. To make predictions, geno2pheno employs decision tree classifiers and support vector machines. View full abstract»

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  • Clips from the computists' weekly

    Page(s): 95 - 98
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    First Page of the Article
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Aims & Scope

IEEE Intelligent Systems serves users, managers, developers, researchers, and purchasers who are interested in intelligent systems and artificial intelligence, with particular emphasis on applications.

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Meet Our Editors

Editor-in-Chief
Daniel Zeng
University of Arizona