Micheal Hewett

Integrating genotype and phenotype information : an overview of the pharmGKB project

Pharmacogenetics seeks to explain how people respond in different ways to the same drug treatment. A classic example of the importance of pharmacogenomics is the variation in individual responses to the anti-leukemia drug, 6-mercaptopurine. Most people metabolize the drug quickly. Some individuals, with a genetic variation for the enzyme thiopurine methyltransferase (TPMT),1 do not. Consequently, they need lower doses of 6-mercaptopurine for effective treatment as normal doses can be lethal. One of the many promises of the human genome project is an ability to pharmacologically treat individuals in a more personalized rather than statistical manner.

Automating Data Acquisition into Ontologies from Pharmacogenetics Relational Data Sources Using Declarative Object Definitions and XML

Ontologies are useful for organizing large numbers of concepts having complex relationships, such as the breadth of genetic and clinical knowledge in pharmacogenomics. But because ontologies change and knowledge evolves, it is time consuming to maintain stable mappings to external data sources that are in relational format. We propose a method for interfacing ontology models with data acquisition from external relational data sources. This method uses a declarative interface between the ontology and the data source, and this interface is modeled in the ontology and implemented using XML schema. Data is imported from the relational source into the ontology using XML, and data integrity is checked by validating the XML submission with an XML schema. We have implemented this approach in PharmGKB (http://www.pharmgkb.org/), a pharmacogenetics knowledge base. Our goals were to (1) import genetic sequence data, collected in relational format, into the pharmacogenetics ontology, and (2) automate the process of updating the links between the ontology and data acquisition when the ontology changes. We tested our approach by linking PharmGKB with data acquisition from a relational model of genetic sequence information. The ontology subsequently evolved, and we were able to rapidly update our interface with the external data and continue acquiring the data. Similar approaches may be helpful for integrating other heterogeneous information sources in order make the diversity of pharmacogenetics data amenable to computational analysis.

Integrating Vision and Spatial Reasoning for Assistive Navigation

This paper describes the goals and research directions of the University of Texas Artificial Intelligence Labs Intelligent Wheelchair Project (IWP). The IWP is a work in progress. The authors are part of a collaborative effort to bring expertise from knowledge representation, control, planning, and machine vision to bear on this difficult and interesting problem domain. Our strategy uses knowledge about the semantic structure of space to focus processing power and sensing resources. The semi-autonomous assistive control of a wheelchair shares many sub-problems with mobile robotics, including those of sensor interpretation, spatial knowledge representation, and real-time control. By enabling the wheelchair with active vision and other sensing modes, and by application of our theories of spatial knowledge representation and reasoning, we hope to provide substantial assistance to people with severe mobility impairments.

Distributing Intelligence within an Individual

Distributed artificial intelligence (DAI) refers to systems in which decentralized, cooperative agents work synergistically to perform a task. Alternative DAI models resemble particular biological or social systems, such as teams, contract nets, or societies. Our DAI model resembles a single individual, characterized by adaptability, versatility, and coherence. The proposed DAI architecture comprises a hierarchy of loosely coupled agents for specific perception, action, and reasoning functions, all operating under the supervision of a top-level control agent. We demonstrate the proposed architecture in the Guardian system for intensive-care monitoring.

ONTOLOGY DEVELOPMENT FOR A PHARMACOGENETICS KNOWLEDGE BASE

Research directed toward discovering how genetic factors influence a patients response to drugs requires coordination of data produced from laboratory experiments, computational methods, and clinical studies. A public repository of pharmacogenetic data should accelerate progress in the field of pharmacogenetics by organizing and disseminating public datasets. We are developing a pharmacogenetics knowledge base (PharmGKB) to support the storage and retrieval of both experimental data and conceptual knowledge. PharmGKB is an Internet-based resource that integrates complex biological, pharmacological, and clinical data in such a way that researchers can submit their data and users can retrieve information to investigate genotype-phenotype correlations. Successful management of the names, meaning, and organization of concepts used within the system is crucial. We have selected a frame-based knowledge-representation system for development of an ontology of concepts and relationships that represent the domain and that permit storage of experimental data. Preliminary experience shows that the ontology we have developed for gene-sequence data allows us to accept, store, and query data submissions.

Sophia: a flexible, Web-based knowledge server

Sophia is a frame based knowledge server built on a commercial relational database system. The system is thus simple and inexpensive, while also providing some advanced functionality. Sophia is accessible to users through the Web or to client applications through an API.

A language and architecture for efficient blackboard systems

The authors present a low-level language for blackboard systems. They also present efficient blackboard activation and agenda maintenance mechanisms. The reason why RETE-like pattern-matching networks are not appropriate for blackboard systems is explained. Instead, activation demons provide an efficient mechanism for agenda maintenance. The results show a significant speedup in agenda maintenance and execution when using the new mechanism in the BB1 blackboard architecture.<<ETX>>

Efficiency Mechanisms for a Class of Blackboard Systems

This paper presents efficient mechanisms for activation, execution and rating that are suitable for use in BB1-style blackboard architectures. We describe a knowledge source compiler that produces match networks and demons for efficient activation and rating while compiling the entire system for increased execution speed. Experiments using the enhancements in a general-purpose blackboard shell illustrate approximately a doubling of run time speed, including an increase in activation speed by a factor of 7.6 on the average. We have also resolved a subclass of blackboard systems that can be compiled down to the machine level by using a condensed representation where low-level blackboard accesses are replaced by vector references. Our analysis shows that the time complexity of the execution cycle of a condensed system is faster than the conventional approach by the ratio of the time required for blackboard retrievals to the time required for vector element retrievals. In practice, this ratio is approximately four orders of magnitude.

A knowledge-based framework for automated software synthesis

Automated software synthesis is one of the central techniques used in knowledge-based software engineering to enhance the quality and efficiency of software development. Although many software synthesis systems have been developed, automatic control of these systems remains a difficult problem. Our goal is to reduce user interaction in transformational and schema-based synthesizers by means of significant advances in control mechanisms. This paper describes an approach for synthesis control that integrates a blackboard control architecture with an existing synthesis system. We present a framework language called MetaMorphos that allows explicit representations of control knowledge for use in selecting appropriate synthesis actions. MetaMorphos represents control decisions explicitly in terms of actions, events, and states. It is task-specific and contains knowledge about programming and how to select synthesizing methods based on given features. By employing a blackboard control architecture, our synthesis controller provides adaptability for dynamic control behaviors and flexibility to handle unanticipated situations during software development. Applying MetaMorphos in the domain of software synthesis, we illustrate how we use MetaMorphos to select appropriate transformations, data structure and algorithm schemas during the synthesis. An example shows how MetaMorphos handles the difficult problem of selecting schemas for two very similar problems which, in the best case, require different solutions.

Automated schema selection in software synthesis

Automated software synthesis is one of the primary methods used in knowledge-based software engineering. Although many software synthesis systems have been successfully designed and implemented, automatic control of these systems remains a difficult problem. This paper describes a task-specific framework called MetaMorphos that allows explicit representations of control knowledge about the programming task and contains a selection method based on a given set of features. We apply MetaMorphos in the domain of schema-based software synthesis systems and illustrate how we use MetaMorphos to select data structure and algorithm schemas. An example shows how MetaMorphos handles the difficult problem of selecting schemas for two very similar problems which, in the best case, require different solutions.<<ETX>>