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Patents and Disclosures




Cancer Informatics in Post Genomic Era

Jurisica, I., D. A. Wigle, and B. Wong. Cancer Informatics in the Post Genomic Era; Toward Information-Based Medicine
Series: Cancer Treatment and Research, Volume 137, Springer Verlag, July 2007.

Less than 50% of diagnosed cancers are cured using current treatment modalities. Many common cancers can already be fractionated into such therapeutic subsets with unique prognostic outcomes based on characteristic molecular phenotypes. It is widely expected that treatment approaches of complex cancer will soon be revolutionized by combining molecular profiling and computational analysis, which will result in the introduction of novel therapeutics and treatment decision algorithms that target the underlying molecular mechanisms of cancer.

The sequencing of the human genome was the first step in understanding the ways in which we are wired.  However, this genetic blueprint provides only a “parts list”, and neither information about how the human organism is actually working, nor insight into function or interactions among the ~30 thousand constitutive parts that comprise our genome. Considering that the 30 years of worldwide molecular biology efforts have only annotated about 10% of this gene set, and we know even less about proteins, it is comforting to know that high-throughput data generation and analysis is now widely available.

By arraying tens of thousands of genes and analyzing abundance of and interaction among proteins, it is now possible to measure the relative activity of genes and proteins in normal and diseased tissue. The technology and datasets of such profiling-based analyses will be described along with the mathematical challenges that face the mining of the resulting datasets.  We describe the issues related to using this information in the clinical setting, and the future steps that will lead to drug design and development to cure complex diseases such as cancer.

Knowledge Discovery in Proteomics

Jurisica, I. and D. Wigle. Knowledge Discovery in Proteomics. Mathematical & Computational Biology Series, Volume 8, Chapman & Hall/CRC Press, 2006.

Who knows useful things, not many things, is wise. Aeschylus (ca. 525-456 BC)

The nascent fields of bioinformatics and computational biology are currently an odd amalgam of everything from biologists with a computational bent, through physicists and mathematicians, to computer scientists and engineers sifting through the myriad of data and grappling with biological questions. Much of the excitement comes from a collective sense that there is something truly new evolving. Hardware and software limitations are declaring themselves as major challenges to managing and interpreting the avalanche of data from high-throughput biological platforms. This drinking from the fire hydrant'' sensation continues to spark interest and draw technical skill from other domains. As we move forward to true systems biology experimentation, it is increasingly obvious that experts in robotics, engineering, mathematics, physics, and computer science have become key players alongside traditional molecular biology.

Life sciences applications are typically characterized by multimodal representations, lack of complete and consistent domain theories, rapid evolution of domain knowledge, high dimensionality, and large amounts of missing information. Data in these domains require robust approaches to deal with missing and noisy information. Modern proteomics is no exception. As our understanding of protein structure and function becomes ever more complicated, we have reached a point in time where the actual management of data is a major hurdle to knowledge discovery. Many of the browse-through applications of yesterday are clearly not useful for computational manipulation. If the data was not created having data mining and decision support in mind, how well can it serve that purpose?

We felt this book was a timely discussion of some of the key issues in the field. In subsequent chapters we discuss a number of examples from our own experience that represent some of the challenges of knowledge discovery in high-throughput proteomics. This discussion is by no means comprehensive, and does not attempt to highlight all relevant domains. However, we hope to provide the reader with an overview of what we envision as an important and emerging field in its own right by discussing the challenges and potential solutions to the problems presented. We have selected five specific domains to discuss: (1) Mass spectrometry based protein analysis; (2) Protein--protein interaction network analysis; (3) Systematic high-throughput protein crystallization; (4) A systematic and integrated analysis of multiple data repositories using a diverse set of algorithms and tools; and (5) Systems biology. In each of these areas, we describe the challenges created by the type of data produced, and potential solutions to the problem of data mining within the domain. We hope this stimulates even more discussion, and newer and better ways to deal with the problems at hand.

Baker, C.J.O., Butler, G., Jurisica, I. Data Integration in the Life Sciences, 9th International Conference, DILS 2013, Montreal, QC, Canada, July 11-12, 2013. Lecture Notes in Computer Science, Volume 7970 2013, DOI: 10.1007/978-3-642-39437-9, Springer Berlin Heidelberg, 2013.

Holzinger, A. and Jurisica, I. Interactive Knowledge Discovery and Data Mining: State-of-the-Art and Future Challenges in Biomedical Informatics, Volume 8401, LNCS, SOTA, Springer, 2014.

Biomedical research is drowning in data, yet starving for knowledge. Current challenges in biomedical research and clinical practice include information overload . the need to combine vast amounts of structured, semi-structured, weakly structured data and vast amounts of unstructured information . and the need to optimize workflows, processes and guidelines, to increase capacity while reducing costs and improving efficiencies. In this paper we provide a very short overview on interactive and integrative solutions for knowledge discovery and data mining. In particular, we emphasize the benefits of including the end user into the .interactive. knowledge discovery process. We describe some of the most important challenges, including the need to develop and apply novel methods, algorithms and tools for the integration, fusion, pre-processing, mapping, analysis and interpretation of complex biomedical data with the aim to identify testable hypotheses, and build realistic models. The HCI-KDD approach, which is a synergistic combination of methodologies and approaches of two areas, Human.Computer Interaction (HCI) and Knowledge Discovery & Data Mining (KDD), offer ideal conditions towards solving these challenges: with the goal of supporting human intelligence with machine intelligence. There is an urgent need for integrative and interactive machine learning solutions, because no medical doctor or biomedical researcher can keep pace today with the increasingly large and complex data sets -- often called "Big Data".


Journal papers and peer-reviewed book chapters






2009 2008 2007







(OLDER) Refereed Conferences


  • Niu, Y. and Jurisica, I., Detecting protein-protein interaction sentences using a mixture model, in Natural Language and Information Systems (NLDB'08), Lecture Notes in Computer Science, E. Kapetanios, V. Sugumaran, and M. Spiliopoulou, Editors, Springer Verlag, Berlin, 352-354, 2008.

  • Xia, E., Jurisica, I., J. Waterhouse, V. Sloan. The impact of runtime estimation in accuracy on scheduler performance, IASTED International Conference on Parallel and Distributed Computing and Systems (PDCS 2007), November 19-21, Cambridge, MA, 2007

  • Yan, R., P. C. Boutros, L.Z. Penn, Jurisica, I.. Comparison of machine learning and pattern discovery algorithms for the prediction of human single nucleotide polymorphisms. IEEE International Conference on Granular Computing, IEEE, Silicon Valley, USA, Nov 2-4, 2007.

  • Xia, E., Jurisica, I., J. Waterhouse. CasSim: a Top-level-simulator for Grid Scheduling and Applications, IBM Cascon Conference. 2006.

  • Otasek, D., K. Brown, Jurisica, I.. Confirming protein-protein interactions by te xt mining. SIAM Conference on Text Mining, Bethesda, Maryland, April 2006.

  • Xia, E., Jurisica, I., J. Waterhouse, V. Sloan. Scheduling functional regression tests in IBM DB2. IBM Cascon, 2005.

  • Arshadi, N. and Jurisica, I.. An ensemble of case-based classifiers for high-dime nsional biological domains. In ICCBR'05, Springer-Verlag Press, pp. 21-34, 2005.

  • Arshadi, N. and Jurisica, I.. Feature selection for improving case-based classifiers on high- dimensional data sets. In AAAI FLAIRS, AAAI Press, Menlo Park, pp. 99-104, 2005.

  • E. Xia and I . Jurisica. Effectiveness of grid configurations on application performance. Parallel and Distributed Computing and Systems (PDCS 2004), 2004.

  • Arshadi, N. and Jurisica, I.. Maintaining CBR systems: A machine learning approach. 7th Eu ropean Conference on Case-Based Reasoning (ECCBR'04), 2004.

  • Xia, E. and Jurisica, I.. Optimizing job scheduling in the grid environment. In Proceedings of The Seventh International Conference on Computer Science and Informatics, Predictive Modeling Techniques, pp. 447-451, Research Triangle Park, NC, 2003.

  • Jurisica, I., C. Cumbaa, A. Lauricella, N. Fehrman, C.Veatch, R. Collins, J. Luft, G. DeTitta. Automatic Classification of Protein Crystallization Screens on 1536-well Plates. The Annual Meeting of the American Crystallographic Association (ACA'03), Session on high-throughput crystallography, Cincinnati, OH, July 26-28, 2003.

  • Jurisica, I., Rogers, P., Glasgow, J., Fortier, S., Luft, J., Bianca, D., DeTitta, G.T. Image-Feature Extraction for Protein Crystallization: Integrating Image Analysis and Case-Based Reasoning Thirteenth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-2001), Seattle, WA, 2001, p. 73-80.


    This paper describes issues related to integrating image analysis techniques into case-based reasoning. Although the approach is generic, a high-throughput protein crystallization problem is used as an example. Our solution to the crystallization problem is to store outcomes of experiments as images, extract important image features, and use them to automatically recognize different crystallization outcomes. Subsequently, we use the outcomes of image classification to perform case-based planning of crystallization experiments for new proteins. Knowledge-discovery techniques are used to extract general principles for crystallization. Such principles are applicable to the adaptation phase of case-based reasoning. The motivation for automated image-feature extraction is twofold: \snum{1} the human interpretation/analysis of image content is subjective, and \snum{2} many problem domains require reasoning with large databases of uninterpreted images. In this paper we present the design and implementation of our integrated system, as well as some preliminary experimental results.

  • Jurisica, I. (2000). Building better decision-support systems by using knowledge discovery. Annual Conference of the American Society for Information Science, Chicago, IL, p. 281-291.

  • Luft, J. R., J. Wolfley, M. Bianca, D. Weeks, Jurisica, I., P. Rogers, J. Glasgow, S. Fortier, G. T. DeTitta. (2000). Gearing up for structural genomics: The challenge of hundreds of proteins and hundred of thousands of crystallizaiton experiments per year. The Annual Conference of the American Crystallographic Association (ACA'00), Saint Paul, MN.


    Structural genomics projects promise to produce hundreds of proteins a year for structural analysis.  The challenge to crystal growers is to make some other step in the structural biology enterprise rate-limiting.  Our approach is to combine high throughput (HTP) crystallization setup and evaluation in the wet lab with sophisticated algorithmic analyses of the HTP outcomes in the computer lab for the purposes of recipe prediction.

    In the wet lab we now have the capacity to prepare and evaluate the results of over sixty thousand (61.4K) crystallization experiments a workweek.  Each is a microbatch experiment conducted under paraffin oil.  Pipetting is performed with robots outfitted with 96 or 384 syringes and XYZ translation stages.  High density (1536 well) micro-assay plates hold the experiments.  1536 crystallization cocktails, covering a wide range of crystallizing agents, have been prepared.  Current pipetting protocols allow us to deploy 200 nanoL droplets of protein solution and crystallization cocktails (total drop size 400 nanoL).  Once a micro-assay plate is prepared with paraffin oil and crystallization cocktails it is possible to set protein solution into the wells in less than five minutes, allowing us to work quickly with unstable proteins.  Current total protein requirements are being assessed, but are likely to be in the 10 mg range.  After setup plates are placed on a computer controlled XY table with micron positioning accuracy.  The plates are translated under a megapixel digital camera where images are captured by a framegrabber.  The XY table can accommodate 28 plates (43K experiments) at a time and the camera can record 43K images in approximately twelve hours.

    In the computer lab the images are analyzed automatically to determine the outcomes of the crystallization experiments.  We are developing a standard vocabulary of outcomes that will describe the results:  clear drop, amorphous precipitate, phase separation, microcrystals, crystals, and uncertain outcome.  These outcomes, recorded as a function of time, are the cornerstone of a crystallization database that will contain physical information about individual proteins as well as results of crystallization experiments with those proteins.  Using case-based reasoning algorithms we will identify patterns of similar properties and crystallization outcomes relating two or more proteins in the database.  Our hypothesis is that, given a quantitative measure of similarity between proteins, recipes successfully employed for one protein will be useful starting points for crystallization experiments with similar proteins.  Future work will center upon the most predictive measures of similarity.

  • Luft, J. R., J. Wolfley, M. Bianca, D. Weeks, Jurisica, I., P. Rogers, J. Glasgow, S. Fortier, G. T. DeTitta. (2000). Gearing Up for ~40K Crystallization Experiments a Day: Meeting The Needs of HTP Structural Proteomics Projects. Eighth International Conference on the Crystallization of Biological Macromolecules (ICCBM-8), Sandestin, Florida.


    The medical potential of the various genome projects now underway will be realized when we know not only the sequences of the amino acids coded in open reading frames but also what these ORFs represent, both structurally and functionally.  Structural proteomics will challenge us to grow more and better crystals for diffraction studies.  Our labs are involved in two major aspects of that work:  getting the techniques and equipment in place to do large scale, high thruput crystallization experiments, and assembling the expertise to make sense of all the data that will come from those experiments.

  • Jurisica, I. (2000). Knowledge Organization by Systematic Knowledge Management and Discovery. International Conference of the International Society of Knowledge Organization (ISKO 6), Toronto, Ontario, p. 366-371.


    We need to use dynamic knowledge organization approaches in order to facilitate effective access and use of domain knowledge. Although there are many approaches to knowledge organization available, it is a challenge to systematically organize evolving domains, because it is not feasible to rely only on humans to create relationships among individual knowledge sources. Additional problems arise because knowledge may not be consistently and completely described, and quality control may not always be in place in distributed knowledge environments. In this article we describe a generic approach to knowledge organization by using systematic knowledge management and applying knowledge-discovery techniques. We use a case-based reasoning system, called TA3, as a core component for knowledge management. Application of symbolic knowledge-discovery component of TA3 supports three main tasks: system optimization, knowledge evolution and evidence creation. To explain advantages of this approach, we use our experience from biomedical domains.

  • Jurisica, I. and Glasgow, J. (2000). Extending case-based reasoning by discovering and using image features in IVF. ACM Symposium on Applied Computing (SAC'2000) Villa Olmo, Como, Italy, p. 52-59.


    This paper describes the application of automated image analysis to evaluate morphology and developmental features of oocytes and embryos in the domain of in vitro fertilization (IVF). Although humans can analyze images more flexibly, computer vision techniques make the proc-ess more objective and precise. We propose to use com-puter-based morphometry to precisely and objectively identify developmental features of oocytes and embryos. Extracted morphological information can be linked with symbolic information to better predict pregnancy outcome and suggest further medical procedures. Recognized fea-tures can then be used to support case-based reasoning and knowledge discovery. The combination of image analysis techniques and case-based reasoning can thus serve as: (1) a feature extraction technique; (2) an indexing approach; and (3) an analysis tool. A combination of symbolic and image information can then be used to identify morpho-logical features of oocytes and embryos that are vital for successful IVF. Extracting image features and analyzing them helps to perform knowledge discovery from images. 

  • Jurisica, I, J. Mylopoulos, E. Yu. (1999) Using Ontologies for Knowledge Management: A Computational Perspective. Annual Conference of the American Society for Information Science, Washington, DC, p. 482-496.


    Knowledge management research focuses on the development of concepts, methods, and tools supporting the management of human knowledge. To further this objective, researchers are studying the way organizations, groups and individuals use knowledge in the performance of daily tasks. They are also developing computer-based tools and techniques to support the acquisition, representation, organization, retrieval, analysis and evolution of knowledge in its many forms. The main objective of this paper is to survey some of the primitive concepts that have been used in computer science for the representation of knowledge and summarize some of their advantages and drawbacks. A secondary objective is to relate these techniques to information sciences theory and practice.

    Several research areas within computer science have developed techniques for representing knowledge so that it can be accessed and used by humans and software systems alike. In particular, Artificial Intelligence (AI) has developed techniques for representing knowledge so that it can be exploited by intelligent systems. Databases have focused on techniques, which allow the representation and management of large amounts of simple knowledge, using as vehicles relational databases and related technologies. Software Engineering and Information Systems have developed elaborate techniques for capturing knowledge that relates to the requirements, design decisions and rationale for a software system. We characterize all these techniques in terms of the primitive concepts they offer for representing knowledge within a given class of applications. 

  • Dayani-Fard, H. and Jurisica, I. (1998) Reverse Engineering by Mining Dynamic Repositories. InWorking Conference on Reverse Engineering (WCRE'98), Honolulu, Hawaii.


    This paper presents some preliminary results on applying information retrieval and knowledge-mining techniques to reverse engineering of legacy systems. In order to support a dynamic environment, we take an approach of integrating lightweight tools. Instead of forcing a user to use a fixed environment, our approach provides a basic information repository, which manages information extracted from the documentation and source code. The system stores this information in a graph structure, it supports navigation through the repository, and modification of its structure and annotation. Preliminary evaluation of the proposed approach on the small-size software system is encouraging. 

  • Jurisica, I. (1998) Asynchronous Telemedicine: A Case-Based Reasoning Approach to Knowledge Sharing. InInformation Technology in Community Health (ITCH*98) Conference, Victoria, BC.


    The health care industry faces constant demands to improve quality, extend services, and reduce cost. Telemedicine satisfies these demands by supporting distant consultations. In addition, knowledge-based systems may augment current synchronous telemedicine applications by storing and managing medical experience over time. By providing timely and efficient access to the knowledge repository, knowledge-based systems help to distribute experience, standardize procedures, lower cost, and increase quality of health care services. This facilitates asynchronous telemedicine.

    Our previous experience from using a case-based reasoning system to support specialists in in vitro fertilization domain shows that this paradigm is suitable for building medical knowledge repositories for knowledge sharing. We propose to extend the system to support tele-consultations: (1) between specialists (rare medical cases); (2) between general practitioners and specialists (standard practices); and (3) between health care professionals and patients (generic medical information). This will help to standardize patient examination and treatment practices. In addition, physicians will be able to share experience via remote knowledge repository.

    This paper focuses on extensions for specialists. We show how case-based reasoning can support evidence-based medicine, remote consultations, and improve knowledge sharing and domain understanding. 

  • Mylopoulos, J, Jurisica, I. and Yu, E. (1998) Computational mechanisms for knowledge organization. In 5th International Conference of the International Society of Knowledge Organization (ISKO 5), pages 125-132, Lille, France. ISKO'


    This paper reviews several knowledge organization techniques used in Computer Science, in areas such as Artificial Intelligence, Databases and Software Engineering. Some of these computational mechanisms may assist in the organization and management of immense digital information resources. At the same time, the paper notes an increasing need for computer-based information systems to operate in open networked environments. This need requires knowledge organization principles, which are flexible and can be used with informally expressed knowledge. We expect to find such knowledge organization techniques in Library and Information Sciences, and hope to integrated them with the computational techniques described in this paper.  

  • Jurisica, I. and Glasgow, J. (1998). An efficient approach to iterative browsing and retrieval for case-based reasoning. Editor Angel Pasqual del Pobil, Jose Mira and Moonis Ali,Lecture Notes in Computer Science, IEA/AIE*98, pages 535-546, Springer-Verlag. IEA/AIE'


    A case base is a repository of past experiences that can be used for problem solving. Given a new problem, expressed in the form of a query, the case base is browsed in search of "similar" or "relevant" cases. One way to perform this search involves the iterative evaluation of a series of queries against the case base, where each query in the series is obtained by restricting or relaxing the preceding query.

    The paper considers alternative approaches for implementing iterative browsing in case-based reasoning systems, including a naive algorithm, which evaluates each query independent of earlier evaluations, and an incremental algorithm, which reuses the results of past query evaluations to minimize the computation required for subsequent queries. In particular, the paper proposes an efficient algorithm for case base browsing and retrieval using database techniques for view maintenance. In addition, the paper evaluates the performance of the proposed algorithm with respect to alternative approaches considering two perspectives: (1) experimental efficiency evaluation using diverse application domains, and (2) scalability evaluation using the performance model of the proposed system.

  • Jurisica, I. and Nixon, B. (1998) Building quality into case-based reasoning systems. Lecture Notes in Computer Science, CAiSE*98,  pages 363-380, Springer-Verlag. CAiSE'


    Complex decision-support information systems for diverse domains need advanced facilities, such as knowledge repositories, reasoning systems, and modeling for processing interrelated information. System development must satisfy functional requirements, but must also systematically meet global quality factors, such as performance, confidentiality and accuracy, called non-functional requirements (NFRs).

    Case-based reasoning (CBR) systems, an important class of decision support systems, require a design process that systematically produces high-quality applications. Beyond satisfying basic functional requirements for CBR, it is important to meet global quality factors, such as performance and confidentiality, called non-functional requirements (NFRs). This paper presents a goal-oriented, knowledge-based approach for aiding decision support system development and usage, namely, it proposes an approach for dealing with non-functional requirements (NFRs) for CBR systems. We show how quality can be built into a CBR system, using the "QualityCBR" approach, which integrates existing work on CBR and NFRs. We illustrate the use of the approach in a complex medical domain in vitro fertilization. In this domain, a CBR system is used for: (1) suggesting hormonal therapy for in-vitro fertilization patients, (2) predicting the probability of successful pregnancy, and (3) interactively determining important patient's characteristics that can improve pregnancy rate. The QualityCBR approach is used to address important NFRs, such as performance, accuracy and confidentiality. 

  • Jurisica, I. Similarity-Based retrieval for diverse Bookshelf software repository users In IBM CASCON Conference, pages 224-235, Toronto, Canada, 1997. CASCON'


    The paper presents a similarity-based retrieval framework for a software repository that aids the process of maintaining, understanding, and migrating legacy software systems. Designing a software repository involves three issues: (1) information content; (2) information representation; and (3) strategies for accessing repository artifacts. Given the architecture of a Bookshelf software repository, we extend the retrieval system to support imprecise queries, iterative browsing, and diverse users. Because of repository size, complexity of queries and relations among artifacts, we take a performance approach to support a scalable implementation. We propose a retrieval system that uses numeric and semantically rich context-based similarity. Efficient iterative browsing is based on an incremental query evaluation algorithm from database management systems. Explicitly defined context supports various retrieval strategies and diverse user models. 

  • Jurisica, I. and Gupta, K. Knowledge-based systems for decision support in healthcare. In Digital Knowledge Conference II, Toronto, 1997. 


    This paper introduces a generic approach to knowledge-based decision-support in medicine. We review problems present in medical domains and introduce available solutions. We describe a case-based reasoning system called SpotLight and discuss its advantages when applied to complex medical domains, in vitro fertilization and nephrology.

  • Jurisica, I. and Glasgow, J. A case-based reasoning approach to learning control. In 5th International Conference on Data and Knowledge Systems for Manufacturing and Engineering, DKSME-96, Phoenix, AZ, 1996. DKSME'

  • Jurisica, I. and Glasgow, J. Case-Based Classification Using Similarity-Based Retrieval. In 8th IEEE International Conference on Tools with Artificial Intelligence, Toulouse, France, p. 410-419. TAI'

  • Jurisica, I. TA3: Case-Based Intelligent Retrieval and Advisory Tool. ACM Conference on Society and the Future of Computing. Durango, CO, 1995.

  • Jurisica, I. and Shapiro, H. Case-based reasoning system applied as an advisor for IVF practitioners 51st Annual Conference of the American Society for Reproductive Medicine, Seattle, WA, 1995. ASRM'

  • Greiner, R. and Jurisica, I. A statistical approach to solving the EBL utility problem. In Proc. ofNational Conference on AI, AAAI-92, pages 241-247, San Jose, CA, 1992. AAAI'

  • Symposia (not updated since 2009)

    Thesis work

    Technical reports

    1. Jurisica, I., J. Glasgow, R. Ng, H. Hoos. Integrative Computational Biology, The 3rd Canadian Working Conference on Computational Biology (CCCB'04), IBM Cascon Conference, IBM TR-74-203-8, 2005.

    2. Arshadi, N. and Jurisica, I., Maintaining case-based reasoning in high-dimensional domains using mixture of experts, Technical Report CSRG-490, Department of Computer Science, University of Toronto, Toronto, Ontario, June 2004.

    3. Przulj, N., Corneil, D., Jurisica, I. Modeling Interactome: Scale-Free or Geometric?, Technical Report 321/04, Department of Computer Science, University of Toronto, Toronto, Ontario, 2004.

    4. Jurisica, I.. Data Mining and Knowledge Discovery, IBM Technical Report 74.165-a, IBM Centre for Advanced Studies, Toronto, December 1, 1998.

    5. J. Glasgow and Jurisica, I.. Data Storage, Retrieval and Mining in Biomedical Applications. IBM Technical Report 74.165-b, IBM Centre for Advanced Studies, Toronto, December 1, 1998.

    6. Jurisica, I.. Context-based similarity applied to retrieval of relevant cases. Technical Report DKBS-TR-94-5, University of Toronto, Department of Computer Science, Toronto, 1994.

    7. R. Greiner and Jurisica, I.. An EBL system that (almost) always improve performance. Technical Report, Siemens Corporate Research, Princeton, NJ, 1992.


    1. A. Djebari and I. Jurisica. Next-generation, scalable network visualization and analysis, IBM Cascon, Toronto, Ontario, 2011.

    2. Rosu, D., Jurisica, I., Ng, J., and Lau, A., 2nd Workshop on Practical Ontologies, IBM CASCON, Toronto, Ontario, 2010.

    3. Jurisica, I. Special Session 5: Visualization of biological networks, Intelligent Systems for Molecular Biology, ISMB-10, Boston Massachusetts, 2010.

    4. Rosu, D., Jurisica, I., Ng, J., and Lau, A., 1st Workshop on Practical Ontologies, IBM CASCON, Toronto, Ontario, 2010.

    5. Jurisica, I. and M. McGuffin, User interfaces for visualizing complex data. IBM Cascon, Toronto, Ontario, October 30, 2008.

    6. Jurisica, I. and D. Aldridge. The Fourth Canadian Working Conference on Computational Biology (CCCB’05) – Systems biology. Toronto, Ontario, October, 2005.

    7. Jurisica, I., J. Glasgow, R. Ng, H. Hoos. The Third Canadian Working Conference on Computational Biology (CCCB’04). Toronto, Ontario, October 4, 2004.

    8. Jurisica, I. and M. Hallett. The Second Canadian Working Conference on Computational Biology (CCCB’02). Toronto, Ontario, October 1, 2002.

    9. Jurisica, I. The First Canadian Working Conference on Computational Biology (CCCB’00). Toronto, Ontario, November 12, 2000.

    10. Jurisica, I. And Rigoutsos, I. Knowledge management: Moving from business to technical and scientific domains, IBM CASCON, 1999.

    11. Jurisica, I. Health care for the future, CITO, February 18, 1999.

    12. Glasgow, J. and Jurisica, I.. “Data storage, retrieval and mining in biomedical applications”. In IBM CASCON Conference, IBM CAS, IBM Technical Report 74.165-b, Toronto, 1998.

    13. Jurisica, I. Telemedicine: Where we are and where can we go? In IBM CASCON Conference, IBM CAS Technical Report 74.161, Toronto, Canada, November 10-13, 1997.

    14. Jurisica, I. and K. Gupta. A Framework for medical knowledge management systems. In Digital Knowledge II Conference, Toronto, Canada, October 20-21, 1997.

    Tutorial Presentations

    1. Otasek, D., Jurisica. I. Practical Biological Network Visualization and Analysis, Great Lakes Bioinformatics Conference, GLBIO-12, Ann Arbor, Michigan, May 15-17, 2012.

    2. Jurisica, I. Biological network visualization and analysis, ISMB, Boston, MA, July 9-13, 2010.

    3. Jurisica, I. Interaction networks. The Canadian Bioinformatics Workshop Series, Ed. M. Hallett and M. Suderman, Systems and Network Biology. Toronto, ON June 27-28, 2008.

    4. Jurisica, I., Knowledge Discovery in High-Throughput Biological Domains. Introduction to computational biology, RSFDGrC, Regina, SA, September 1, 2005.

    5. Jurisica, I., I. Rigoutsos, A. Floratos. Knowledge Discovery in Biological Domains, ACM, Knowledge Discovery in Databases Conference, Boston, MA, 2000.

    6. Glasgow, J. and Jurisica, I.. Knowledge Mining and Discovery in Molecular Biology, Pacific Symposium on Biocomputing (PSB’99), Hawaii, January 4, 1999.

    7. Jurisica, I. Data Mining and Knowledge Discovery, IBM CASCON Conference, IBM Technical Report 74.165-a, Toronto, December 1, 1998.

    8. Mylopoulos, J., V. Chaundhri, Jurisica, I., D. Plexousakis, A. Shrufi, T. Topaloglou, and H. Wang. Development and Application of Knowledge Base Management Systems. Australian Joint Conference on AI (AJCAI’95), Canberra, Australia, November 1995.

    9. Mylopoulos, J., V. Chaundhri, Jurisica, I., D. Plexousakis, A. Shrufi, T. Topaloglou, and H. Wang. Knowledge Base Management Systems. International Joint Conference on AI (IJCAI’95), Montreal, Quebec, August 1995.

    10. Mylopoulos J., V. Chaundhri, Jurisica, I., D. Plexousakis, A. Shrufi, T. Topaloglou, and H. Wang. Knowledge Base Management and its Application. IEEE Conference on AI Applications (CAIA’94), IEEE Computer Society, San Antonio, TX, March 1994.

    11. Jurisica, I. Representation and management issues for case-based reasoning systems. TRIO/ITRC Research Retreat, Queen's University, Kingston, May 10-12 1994.

    12. Mylopoulos, J., V. Chaundhri, Jurisica, I., D. Plexousakis, A. Shrufi, T. Topaloglou, and H. Wang. Knowledge Base Management Systems. Database and Expert Systems Applications (DEXA’94), Athens, Greece, September 1994

    13. Mylopoulos, J., V. Chaundhri, Jurisica, I., D. Plexousakis, A. Shrufi, T. Topaloglou, and H. Wang. Information and Knowledge Base Management. Information Technology Research Center, University of Toronto, Department of Computer Science, February 1993.

    Supplementary Data up to 2007