The presentation on bioinformatics defines the field as the use of computers for managing and analyzing biological data, encompassing disciplines like molecular biology and computational biology. It discusses its applications in genomic medicine, various challenges such as data handling, and ethical issues regarding genetic information. The talk highlights the future evolution of bioinformatics, emphasizing the importance of integrating data and addressing legal and social implications.

1. A Hitchhikers Guide to Bioinformatics Drexel University INFO648-900-200915 A Presentation of Health Informatics Group 5 Cecilia Vernes Joel Abueg Kadodjomon Yeo Sharon McDowell Hall Terrence Hughes SlideShare.Net: Click on the Notes tab below to see a transcript of the presentation

2. Goals of this Presentation Provide some definitions Answer the question: Why study it? What has been accomplished it? What challenges exist? Identify what role it plays (how) Relate it to topics from previous weeks Raise issues and questions

3. Bioinformatics: Why study it? Methodological elements Tools and techniques of informatics Understand how it supports applications Non-medical applications Genomic Medicine and the challenges posed Raise awareness for legal, ethical, social issues

4. What is Bioinformatics ? Bioinformatics is the use of computers for the acquisition, management, and analysis of biological information. It incorporates elements of molecular biology, computational biology, database computing, and the Internet… … bioinformatics is clearly a multi-disciplinary field including: computer systems management networking, database design, computer programming, molecular biology From Using Computers for Molecular Biology, Stuart M. Brown, PhD, RCR, NYU Medical Center

5. Bioinformatics is a multifaceted discipline combining many scientific fields including computational biology, statistics, mathematics, molecular biology and genetics (Fenstermacher, 2005, p. 440). … from Bayat (2002), p 1018.

6. Bioinformatics: Origins & Definitions Bioinformatics has many definitions … the study of how information is represented and analyzed in biological systems, starting at the molecular level … concerned with understanding how basic biological systems conspire to create molecules, organelles, living cells, organs, and entire organisms (Altman & Mooney, 2006, p. 763) … application of tools of computation and analysis to the capture and interpretation of biological data (Bayat, 2003, p. 1018)

7. DNA is the nature’s universal information storage medium … increasingly, biological research relies on information science

8. The Human Genome Project Produced the human genome sequence Spawned a new field: genomics Spurred new technologies And now provides us an unparalleled opportunity to apply new knowledge, technologies, and approaches to health care Guttmacher (2009)

9. Bioinformatics supports “-omics” research … from Bayat (2002), p 1020.

10. … from Bayat (2002), p 1020. … from McDaniel, Schutte, & Keller (2008), p. 220

11. Bioinformatics Data From Using Computers for Molecular Biology, Stuart M. Brown, PhD, RCR, NYU Medical Center Bioinformatics deals with any type of data that is of interest to biologists DNA and protein sequences Gene expression (microarray) Raw data collected from field or laboratory experiment Images, virtual models, Software Articles from literature and databases of citations Each type of data can exist in many incompatible computer formats The analysis of DNA sequence data has come to dominate the field of bioinformatics, but the term can be applied to any type of biological data that can be recorded as numbers or images and handled by computers

18. Moore’s Law A Side Note Mooer’s Law

19. 14,000X

20. An information explosion… Lots of data in genome More data in when we attempt to discern structure of data relate to transciptomics, proteomics relate to structure, physiology relate to disease relate to variation Automated discovery, experiments Biomedical knowledge (coming) Clinical knowledge (coming)

21. [Some] Research Projects The Human Genome Project -- old news, 6 years ago International HapMap Project -- www.hapmap.org The 1000 Genomes Project – www.1000.genomes.org Encyclopedia of DNA Elements ( ENCODE ) Project The Cancer Genome Atlas (TCGA) Human Microbiome Project (HMP) – www.hmpdacc.org The eMERGE (Electronic Medical Records and Genomics) Network

22. Common Features of Projects High throughput Use of technology, in particular Automation (Robotics, AI) Databases Visualization, simulation/computational models Groupware: Coordination and communication Public domain tools Open sharing of data

23. Some Challenges Volume of data is staggering How to store and collect sequence information? RDBMSs don’t handle sequence data well Better handled by Object Oriented DBM How to analyze and display the data Automated algorithms Contextual visualization methods Clusters, profiles, etc Sequence data is meaningless without context Not well suited to printed medical record

24. General Informatics Techniques/Tools in Bioinformatics Discovery and Analyses Text String Comparison Text search Statistical analysis Finding Patterns AI / Machine Learning Clustering Data mining Geometric Robotics Graphics (Surfaces, Volumes) Comparison and 3D Matching (Vision, Recognition) Physical Simulation Newtonian Mechanics Electrostatics Numerical Algorithms Simulation Storage Databases Building, Querying Complex data Annotations Citations Standards Interoperability Knowledge Management Classification Vocabularies Ontologies Communications Process Workflow

25. Bioinformatics: Tools Annotation … from Chicurel (2002), p 753-754. user friendly, in the public domain, and increasingly integrated commercial tools streamline tasks, access proprietary databases Visualization

26. Bioinformatics: Why study it? Methodological elements … [Clinical and bioinformatics] share significant methodological elements, so an understanding of the issues in bioinformatics can be valuable for the student of clinical informatics (Altman & Mooney, 2006, p. 763) Understand how it supports applications Non-medical applications Genomic Medicine EMRs, PHRs will need to capture genetic data Clinical research that links genomic and clinical KBs DTC/Consumer informatics: Personalized testing/diagnostics

27. Bioinformatics are essential to many non-medical applications Agriculture Crops resistant to drought and insects More nutritious products Bio pesticides Risk assessment & mitigation Waste cleanup Explore the properties of the bacteria Deinococcus radiodurans for clean up of hazardous waste sites Reduce likelihood of heritable mutations Energy and environment New energy sources: bio fuels Pollutant detection Climate change Carbon sequestration Forensic science DNA Detect criminals by analysis of DNA in crime scenes Mass spectrometry

28. Bioinformatics & Molecular Medicine Early detection of genetic predispositions to diseases Improved diagnosis of disease Pharmacogenomics Customized drugs Individualized drugs selection Better methods for determining drug doses for individuals Appropriate doses determination Gene therapy and control systems for drugs

30. Recent Example: Anticoagulant Dosing Genetic variability among patients plays an important role in determining the dose of warfarin that should be used when oral anticoagulation is initiated, but practical methods of using genetic information have not been evaluated in a diverse and large population. We developed and used an algorithm for estimating the appropriate warfarin dose that is based on both clinical and genetic data from a broad population base.

31. A Fun Fact: How Many Human Genes Do All Current Drugs Target? ~500 (2.5% of the genome) ~1,000 (10%) ~5,000 (25%) ~10,000 (50%) ~ 15,000 (75%) ~20,000 (100%)

32. A Fun Fact: How Many Human Genes Do All Current Drugs Target? ~500 (2.5% of the genome) ~1,000 (10%) ~5,000 (25%) ~10,000 (50%) ~ 15,000 (75%) ~20,000 (100%)

33. Bioinformatics & Drug Discovery … from Luscombe, Greenbaum, Gerstein (2001), p 95.

34. Genomic medicine Goes beyond genetic risk factor of disease Family history Considers genetics in effectiveness of drugs Genomic assays

35. Bioinformatics and clinical informatics: Genomic medicine poses several challenges Clinically relevant information growing very quickly Patients are becoming more involved in the research activities Knowledge support, facilitating professional development in genetics is an obvious role for informatics EMR data informs clinical genomics research, and vice versa ( more on this later ) Standardized language of genomics in clinical work Biomedical Ontologies (bioontology.org) EMRs/PHRs will need to include this EMRs, PHRs may be a location for in silico genome Clinical Decision Support Systems

36. Clinical Decision Support Systems CDSSs will need to include genetic factors, or the results of genetic testing An example: Targeted cancer therapy The appropriateness of adjuvant therapy in breast cancer patients given presence of gene producing the HER2 protein http://www.herceptin.com/her2-breast-cancer/testing-education/what-is.jsp http://www.adjuvantonline.com/

37. Medical Informatics vs. Bioinformatics

38. Getting Respect?? Medical Informatics “ just data sources” but are in fact the product of 30 years of research working to make medical information retrieval a fluid technological system. Bioinformatics professionals outside the field are cited as considering Bioinformatics research to be easy and cheap, yielding free software, and producing rapid publication of easily verified predictions. In truth, Bioinformatics programs use a mixture of mathematical models and expert heuristics in complex software systems.

39. Medical Informatics and Bioinformatics The Differences? Although Medical Informatics and Bioinformatics both exploit computers and computational tools, they differ in many ways. Arguably, these differences are due to diversity in the domain expertise of the practitioners (medicine vs. biology) and researchers involved in the application field (healthcare professionals vs. bio scientists) and the educational emphasis adopted by the independent disciplines (patient-care vs. basic-research). Training Multidisciplinary Biomedical Informatics Students: Three Years of Experience, JAMIA 2008, Mar-Apr; 15(2): 246 .

40. Bioinformatics professionals focus on scientific discovery and use exacting specifications, tools, models, and evaluation criteria. Medical Informatics professionals utilize cognitive reasoning and empirically justified decision support systems. Medical Informatics expertise in developing health care applications and the strength of Bioinformatics in biological “discovery science” complement each other well. Maojo & Kulikowski, p. 515 Vs Symbiotic Relationship

41. eMERGE The eMERGE ( E lectronic Me dical R ecords and Ge nomics) Network is a five-member consortium formed to develop, disseminate, and apply approaches to research that combine DNA biorepositories with electronic medical record (EMR) systems for large-scale, high-throughput genetic research. http://www.mc.vanderbilt.edu/victr/dcc/projects/acc/index.php/About

42. Relating Clinical and Genetic KBs … from Lussier & Sakar (2002), p 470.

43. Bioinformatics & Drug Discovery: Connecting patients with researchers … from Collins (2008) presentation at NCHPEG

44. Bioinformatics meets public health informatics Health literacy Public policy concerning testing

45. Bioinformatics meets consumer health informatics

46. My in-silico genome: There may be an “app for that” http://mobihealthnews.com/2676/slideshow-illuminas-concept-mygenome-iphone-app/

47. Bioinformatics in the future …and biomedical- and other informatics in the future too Web 3.0 is likely to have a big effect on medicine in 2008. In bioinformatics, it will become more common to process ever larger amounts of data. In fact, experts in bioinformatics already search for data from disparate systems, and they have started to build rich semantic relations into information tools for knowledge discovery. Finally, greater capacity for creating knowledge in medicine will be possible if we have the will to publish clinical data openly and transparently, and subject it to scrutiny. Developing a more personalised healthcare system will be an important challenge for doctors in web 3.0. In an era of greater personalisation, treating patients’ health problems according to their genetic profiles will depend on using the latest information technologies. Giustini editorial in BMJ 2007;335:1273-4 doi: 10.1136/bmj.39428.494236. What is Web 3.0? (next 10 years) Semantic web Uses metadata Establish authority (wisdom of crowd vs. experts) Ontologies, semantic systems Knowledge discovery

48. Ethical, legal, and social issues ETHICAL who controls acquisition of a person’s DNA and the information it contains; Consent : what uses may be made of that information; who decides how data are used; Privacy, de-identification : How to do this fairly, acknowledging ownership consent, and privacy (Is genomic privacy even possible?) LEGAL Prevent discrimination based on genomic data (cf. Genetic Information Nondiscrimination Act (GINA) of 2008 Other regulatory frameworks that may public genomics possible SOCIAL Expand access to ensure generalizability (address sample bias) and pursue patient (as opposed to basic research) agendas Consider benefits and costs of open or public genomic models

49. In the beginning, there was Gregor Mendel 1865 Gregor Mendel and his pea plants 1953 Watson & Crick published their article in “ Nature” detailing DNA’s 3D structure 1984 DNA fingerprinting invented 2003 Human Genome Project completed Largest funding for bioethics to date 2005 HapMap Project completed 2008 The Genetic Information Non-Discrimination Act (GINA) signed into law

50. Ethical, Legal and Social Implications (ELSI) Research Program Established at the same time as the Human Genome Project. Researchers knew there could be major issues with the genetic information obtained Still exists as part of the project and research continues to this day Issues brought up during the project are now used to educate the public

51. Genetic Information Non-Discrimination Act (GINA) President Bush signed into law in 2008 Protects rights of individuals from the misuse or discrimination that could come from knowledge of their risk for disease or conditions based upon genetic information Uncertainty if the value of GINA is misplaced or could be abused Physicians are free to practice good medicine by offering the genetic tests to patients Clinical research records concerned potential study patients and how their information would or could be used

52. A Real Life Case Swabbing for a Job University of Akron rescinded their requirement of potential employees to provide a DNA sample “ appears to violate a federal law that takes effect on November 21 called the Genetic Information Nondiscrimination Act , better known as GINA. It also could conflict with the Americans with Disabilities Act.”

53. Hypothetical Cases to Consider Nurse immune to Ebola gives blood sample that later becomes grounds for creating a cure/vaccine against Ebola. Is she entitled to royalties from the pharmaceutical company that used her blood and developed the product?

54. Summary of this Presentation Bioinformatics has many definitions Its study is useful Methodology of informatics Clinical connections Bioinformatics data poses challenges Technical Ethical, legal, social

55. Questions for Discussion In light what we have learned with electronic health records systems, what challenges do you see in terms of data integration in Bioinformatics? If you were considering marrying someone (and did not plan on having children), would you want him or her to provide you an analysis of their genome? What if your potential future partner asked this of you? What may be the social, cultural, and genetic implications of genome information in information utilization in mate selection? The Genetic Information Nondiscrimination Act (“GINA”) becomes effective November 21, 2009, and provides new protections against the improper use of genetic information. How will employee sponsored wellness programs, particularly those that require a self-reported health risk appraisal, be affected? Does GINA provide sufficient protections against all potential misuse of information? Debate what "misuse" might be. For example, if parts of your genome are critical for development of some new treatment, would it be right for it or some derived work to be patented? Assume that health care reform is passed, and a basic set of benefits is mandated. “Personalized medicine” can lead to more effective treatments, but there are costs to determine what is effective for an individual. For example, the cost of some genetic assays for breast cancer are on the order of $5000. Should these tests be a covered benefit, even it if increases overall costs--why or why not?

56. References Altman, R. B., & Mooney, S. D. (2006). Bioinformatics. In E. H. Shortliffe & J. J. Cimino (Eds.), Biomedical Informatics (pp. 763-789). New York, NY: Springer. Bayat, A. (2002). Bioinformatics. British Medical Journal, 324, 1018-1022. Brown, S. M. (2009). Using computers for molecular biology. NYU Medical Center Course G16.2604. Retrieved from http://www.med.nyu.edu/rcr/rcr/course/intro-bioinf.html Chicurel, M. (2002). Bioinformatics: Putting it all together. Nature, 419, 751-757. Collins, F. (2009, September). NIH, genomics, and health. Presentation at the NCHPEG 2009 Annual Meeting (see website for download). ELSI Research Program. (Nov. 6, 2009) Retrieved November 11, 2009, from http://www.genome.gov/10001618 Fenstermacher, D. (2005). Introduction to bioinformatics. Journal of the American Society for Information Science and Technology, 56(5 ), 440-446. Giustini, D. (2007). Web 3.0 and medicine. British Medical Journal, 335 , 1273-1274. Goodman, N. (2002). Biological data becomes computer literacy: new advances in bioinformatics. Current Opinion in Biotechnology, 13, 68-71. Guttmacher, A. E. (2009, September). The future of human genome research and its implications for the education of health professionals. PowerPoint presentation at the NCHPEG 2009 Annual Meeting (see website for download). Hudson, K.L., Holohan, M.K., & Collins, F. S. (2008). Keeping pace with the times--the Genetic Information Nondiscrimination Act of 2008. The New England Journal of Medicine. 358 (25), 2661-3. http://content.nejm.org/cgi/content/full/358/25/2661/DC1 Jaschik, S. (2009, Oct 29). DNA Swab for Your Job. Inside Higher Ed. Retrieved from November 11, 2009 from, http://www.insidehighered.com/news/2009/10/29/akron Lussier, Y. A., Sarkar, I. N., & Cantor, M. (2002). An integrative model for in-silico clinical-genomics discovery science. AMIA 2002 Annual Symposium Proceedings, 469-473. Magio, V. (2003). Bioinformatics and medical informatics: Collaborations of the Road to Genomic Medicine. Journal of the American Medical Informatics Association, 10(6), 515-522. McDaniel, A. M., Schutte, D. L., & Keller, L. O. (2008). Consumer health informatics: From genomics to Population health. Nursing Outlook, 56 , 216-223. National Human Genome Research Institute. A guide to your genome. Online Education Kit: Ethical, Legal and Social Implications of Genetic Knowledge. (Feb 13, 2009). Retrieved November 11, 2009 from, http://www.genome.gov/25019880 “ Prohibiting Discrimination Based on Genetic Information; Interim Final Rules; HIPAA Administrative Simplification; Genetic Information Nondiscrimination Act; Proposed Rules” Federal Register 74:193 (October 7, 2009) p.51644-51697; Available from http://edocket.access.gpo.gov/2009/pdf/E9-22504.pdf ; Accessed 11/13/09. Ramoni, M. F. (2003). Population genetics in the post-genomic era. Presentation for HST950J Medical Computing. Boston, MA: Harvard University-MIT. Robertson, J. A. (2003). The $1000 genome: Ethical and legal issues in whole genome sequencing of individuals. The American Journal of Bioethics, 3(3): Infocus. http://www.bioethics.net U.S. Department of Health and Human Services. (October 1, 2009). New Rules Protect Patient’s Genetic Information. U.S. Department of Health and Human Services. Retrieved November 13, 2009 , from the World Wide Web: http://www.hhs.gov/news/press/2009pres/10/20091001b.html Van Mulligen, E. M., Cases, M., Hettne, K.., Molero, E., Weeber, M., Robertson, K. A., Oliva, B., de la Calle, G., & Maojo., V., (2008). Training multidisciplinary biomedical informatics students: Three years of experience. Journal of the Medical Informatics Association, 15 (2), 246-254.

57. Useful Bioinformatics Websites (Bayat, 2003) National Center for Biotechnology Information (www.ncbi.nlm.nih.gov)—maintains bioinformatic tools and databases National Center for Genome Resources (www.ncgr.org/)—links scientists to bioinformatics solutions by collaborations, data, and software development Genbank (www.ncbi.nlm.nih.gov/Genbank)—stores and archives DNA sequences from both large scale genome projects and individual laboratories Unigene (www.ncbi.nlm.nih.gov/UniGene)—gene sequence collection containing data on map location of genes in chromosomes European Bioinformatic Institute (www.ebi.ac.uk)—centre for research and services in bioinformatics; manages databases of biological data Ensembl (www.ensembl.org)—automatic annotation database on genomes BioInform (www.bioinform.com)—global bioinformatics news service SWISS­PROT (www.expasy.org/sprot/)—important protein database with sequence data from all organisms, which has a high level of annotation (includes function, structure, and variations) and is minimally redundant (few duplicate copies) International Society for Computational Biology (www.iscb.org/)—aims to advance scientific understanding of living systems through computation; has useful bioinformatic links Appendix B: Website List 1

58. Useful Bioinformatics Websites from “Group 5” US National Institutes of Health Roadmap for Research in Bioinformatics and Computational Biology (nihroadmap.nih.gov/bioinformatics/) National Human Genome Research Institute (genome.gov) National Coalition for Health Professional Education in Genetics ( www.nchpeg.org ) NIH Roadmap for Research in Bioinformatics and Computational Biology (nihroadmap.nih.gov/bioinformatics/) Genomics Law Report ( www.genomicslawreport.com ) Others will posted on our wiki Appendix B: Website List 2

59. Appendix C: Some Terms to Know Alleles – form of the same gene with small differences in their sequence of DNA bases ( http://ghr.nlm.nih.gov/handbook/basics/gene ) Gene – A hereditary unit consisting of a sequence of DNA that occupies a specific location on a chromosome and determines a particular characteristic in an organism (p 943 – Chapter on Biomedical Informatics) Genome – … all of an organism's genetic material. ( http://publications.nigms.nih.gov/thenewgenetics/glossary.html#systemsbiology ) Microarray ( gene chip or a DNA chip ) Microarrays consist of large numbers of molecules (often, but not always, DNA) distributed in rows in a very small space. Microarrays permit scientists to study gene expression by providing a snapshot of all the genes that are active in a cell at a particular time. ( http://publications.nigms.nih.gov/thenewgenetics/glossary.html#systemsbiology ) DNA , - Deoxyribonucleic acid, is the hereditary material in humans and almost all other organisms. ( http:// ghr.nlm.nih.gov/handbook/basics/dna ) RNA – Ribonucleic acid – the building block of proteins -- is a molecule similar to DNA. Unlike DNA, RNA is single-stranded. An RNA strand has a backbone made of alternating sugar (ribose) and phosphate groups. Attached to each sugar is one of four bases--adenine (A), uracil (U), cytosine (C), or guanine (G). Different types of RNA exist in the cell: messenger RNA (mRNA), ribosomal RNA (rRNA), and transfer RNA (tRNA). More recently, some small RNAs have been found to be involved in regulating gene expression. ( http:// ghr.nlm.nih.gov /glossary= rna ) Transcription – The first major step in gene expression, in which the information coded in DNA is copied into a molecule of RNA. ( http://publications.nigms.nih.gov/thenewgenetics/glossary.html#systemsbiology ) Translation – The second major step in gene expression, in which the instructions encoded in RNA are carried out by making a protein or starting or stopping protein synthesis. ( http://publications.nigms.nih.gov/thenewgenetics/glossary.html#systemsbiology ) SNP _ also called “snips” – a variation of a single base pair (Single Nucleotide Polymorphism) A DNA sequence variation that occurs when a single nucleotide in the genome is altered. For example, an SNP might change the nucleotide sequence AAGC C TA to AAGC T TA. A variation must occur in at least 1% of the population to be considered an SNP. (p 985 -- Chapter on Biomedical Informatics)

60. Bioterrorism Bioterrorism employs biological weapons to inflict damage on human populations, livestock and the environment. It is largely a matter of microbiology, principally involving the use of micro-organisms and/or their toxins. Appendix D: Bioterrorism

61. Perception of Bioterrorism risk: Developed Countries most concerned about anthrax, botulism, pneumonic plague, tularemia, and smallpox. Developing countries concerned with cholera, pneumonic plague, tularemia, smallpox, hemorrhagic viral infections and other contagious diseases. Economic concern that animal and plant diseases or pests may be introduced into the food chain. Are Our Defences Against Bioterrorism Adequate? C Kameswara Rao

62. National Electronic Disease Surveillance System (NEDSS) This broad initiative is designed to : To detect outbreaks rapidly and to monitor the health of the nation Facilitate the electronic transfer of appropriate information from clinical information systems in the health care system to public health departments Reduce provider burden in the provision of information Enhance both the timeliness and quality of information provided

63. Health Surveillance Systems The Centers for Disease Control & Prevention evaluates surveillance systems on the following: Indexing of frequency, severity, disparities, associated costs, preventability, potential clinical course and public interest. Purpose and objectives Planned uses of the data Case definition/event under surveillance Legal authority for data collection Organizational home of system Level of integration with other systems Flowchart Description (population, interval of data collection, data collected, reporting sources, data management, data analysis and dissemination, patient privacy, confidentiality, and system security, and records management) Personnel requirements Funding sources Other resources CDC table as quoted in “Roundtable on Bioterrorism Detection by W.B. Lober, et al

64. Is the Evaluation System Deficient? A study concerned with the evaluation methods of detection systems & diagnostic decision support systems found in 2004: Of 35 evaluated systems, --only 4 systems reported both sensitivity and specificity --13 were evaluated against a reference standard --31 systems evaluated for timeliness Most evaluations of detection systems and some evaluations of diagnostic systems for bioterrorism responses are critically deficient. Because false-positive and false-negative rates are unknown for most systems, decision making on the basis of these systems is seriously compromised. “ Evaluating Detection and Diagnostic Decision Support Systems for Bioterrorism Response” (D. Bravata , et al (100-108).

65. Evaluation Methods Both Sensitivity & specificity of the data must be measured relative to an appropriate reference standard. Reference standard should be applied to all samples, whether positive or negative. The tests should be evaluated blind to the results of the reference standard. Samples or patient population needs to resemble the populations in which the system will be used . detection systems should be evaluated under the most realistic conditions possible, which may be difficult for bioterrorism agents as conditions can range from hoaxes with no cases to real situations with a number of cases.

66. References for Bioterrorism Section Are Our Defences Against Bioterrorism Adequate? C Kameswara Rao http://fbae.org/2009/FBAE/website/special-topics_bioterrosim_are_our_defences_against.html Bioinformatics and Medical Informatics: Collaborations on the road to genomic medicine? V. Maojo, MD, C.A. Kulikowski. Journal of the American Medical Informatics Association 10(6), Nov/Dec 2003, 515-521. Roundtable on Bioterrorism Detection. W. B. Lober, MD, et al. Journal of the American Medical Informatics Association 9(2), Mar/Apr 2002, 105-115.