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Journal of Biomedical Informatics 40 (2007) 67–72

An information technology emphasis in biomedicalinformatics education

Michael D. Kane *, Jeffrey L. Brewer

Department of Computer and Information Technology, Purdue University, West Lafayette, IN 47907-1421, USA

Received 5 December 2005Available online 9 March 2006

Abstract

Unprecedented growth in the interdisciplinary domain of biomedical informatics reflects the recent advancements in genomicsequence availability, high-content biotechnology screening systems, as well as the expectations of computational biology to commanda leading role in drug discovery and disease characterization. These forces have moved much of life sciences research almost completelyinto the computational domain. Importantly, educational training in biomedical informatics has been limited to students enrolled in thelife sciences curricula, yet much of the skills needed to succeed in biomedical informatics involve or augment training in informationtechnology curricula. This manuscript describes the methods and rationale for training students enrolled in information technology cur-ricula in the field of biomedical informatics, which augments the existing information technology curriculum and provides training onspecific subjects in Biomedical Informatics not emphasized in bioinformatics courses offered in life science programs, and does notrequire prerequisite courses in the life sciences.� 2006 Elsevier Inc. All rights reserved.

Keywords: Biomedical informatics; Bioinformatics; Information technology; Education; Interdisciplinary

1. Introduction

The growth of the biotechnology industry in recentyears is unprecedented, and advancements in molecularmodeling, disease characterization, pharmaceutical discov-ery, clinical healthcare, forensics, and agriculture funda-mentally impact economic and social issues worldwide.Research and discovery activities in the life sciences wereonce limited to a single gene or protein, but thedevelopment of computational and information systems(integrated with biotechnology) has facilitated a shift tohigh-throughput screening (thousands of samples perday) and high-content detection systems (thousands ofdata points per sample), and the supporting informationsystem represents the enabling factor in these endeavors.The National Center for Biotechnology Information(NCBI) maintains a growing collection of databases

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doi:10.1016/j.jbi.2006.02.006

* Corresponding author. Fax: +1 765 496 1212.E-mail address: mdkane@purdue.edu (M.D. Kane).

housing genetic sequence and protein structure/activitydata (among other biological data sets), which arecurrently growing at an exponential rate. On February15, 2005, the NCBI published its 146th release of GenBank,a flat-file database of gene sequences, which contains42,734,478 gene sequences [1]. In addition, genomicresearch enjoyed a landmark accomplishment withthe completion and publication of the human genome inFebruary, 2001 [2,3]. This represents one of two fundamentaladvancements in the last 10 years that have moved genomicresearch almost completely into the computationaldomain, and dedicated information systems now representthe enabling factor for life sciences research.

As mentioned, the human genome has been completelysequenced and published [2,3], yet the boon of high-throughput gene sequencing is not limited to the humanspecies. Many other genomes have been completelysequenced representing a breadth of mammalian, agricul-tural, viral and bacterial organisms, and this fundamentaladvancement in genomic data availability offers scientists

68 M.D. Kane, J.L. Brewer / Journal of Biomedical Informatics 40 (2007) 67–72

the informational basis of living systems. The other funda-mental breakthrough in biotechnology can best bedescribed as ‘‘high-content’’ genomic screening, where theintegrity (single nucleotide polymorphisms) and activity(gene expression profiling) of every gene in a known gen-ome can be detected from a single biological sample. Thisis accomplished using the DNA microarray technologyplatform, which can detect tens of thousands of genes usinga small functionalized system the size of a postage stamp[4–6]. The information flowing from genomic laboratoriesusing DNA microarray technology constitutes hundredsof thousands of gene-specific measurements each day.The overall impact of this revolutionary technologydepends upon an integrated information system to analyzeand store the data, as well as computational systems todesign each of these gene-specific detections (hybridiza-tions). The Food and Drug Administration (FDA) hasrecently published guidelines for the development of bio-technology methods, such as the DNA microarray plat-form, for use in genome-based prognostics anddiagnostics in humans [7], which marks the beginning ofa new era in healthcare that utilizes a patient’s genomesequence to enable ‘‘personalized medicine.’’

With this explosion of molecular data and biotechnolo-gy capabilities, the pharmaceutical, biotechnology, andhealthcare industries are dependent on professionals withinformation systems and technology skills to fully exploitthese resources and translate molecular and cellular datainto new genetic and therapeutic discoveries, as well asdevelop new biotechnologies to impact human health. Sim-ilarly, information technology professionals must betrained in the utilization of biomedical data structuresand capable of developing and integrating information sys-tems with biotechnology systems to meet this challenge.

Given that bioinformatics has evolved to meet the objec-tives of life sciences research, courses and curricula thatoffer training in bioinformatics, or biomedical informatics[8], have primarily originated in life science programswhere traditional life sciences training in genetics, genomicsand proteomics is augmented with training in sequencealignments, genomic data analysis and protein modeling.This training largely involves the use of existing softwareapplications dedicated to queries in gene and proteinsequence databases. Yet this training is not appropriatefor students enrolled in information technology programsthat lack what is considered prerequisite knowledge in biol-ogy and genetics. However, students enrolled in informa-tion technology programs are developing skills that aredirectly applicable to biomedical informatics such as dataformats, database structure and development, applicationsdevelopment, and systems design. Given that both fields ofstudy are important to training in biomedical informatics;interdisciplinary training must be developed that developskills relevant for both groups of students.

It is important to mention that the terminology utilizedfor training information technology students is describedherein as ‘‘biomedical informatics’’ since the learning

objective of this training is to apply information sciencesand technology skills to all subdisciplines within thisdomain, and aspects from all defined subdisplines areincluded in the courses described. It is recognized that‘‘bioinformatics’’ is a subdiscipline of ‘‘biomedical informat-ics’’ [8], and the primary emphasis of subject matter in thecourse series falls within the realm of bioinformatics. Thislarger emphasis on bioinformatics is intentional simply toprovide the necessary background in areas most lacking inexisting information technology education, since studentshave little or no background in life sciences. Furthermore,the author’s institution does not harbor a medical schoolor clinical research environment, and information technolo-gy students are much more likely to apply their skills to inde-pendent or course-directed projects in the basic life sciencesduring their undergraduate and graduate education. Thisapproach addresses the lack of knowledge of biology thatis typical of information technology students and providesthe fundamental aspects of biotechnology, genomics andstatistics, which augment skills in information technology,ultimately to design, develop and implement discoverysupport information systems, while providing exposure toongoing research projects across campus for demonstrationpurposes and student project activities.

The authors are involved in a campus-wide graduate-level interdisciplinary Computational Life Sciences pro-gram that involves a distinct (and growing) list of academicdepartments including Computer and Information Tech-nology, Biological Sciences, Statistics, Chemical Engineer-ing, Computer Sciences, Agronomy, and Electrical &Computer Engineering. This program offers graduate stu-dents in any department a breadth of training opportuni-ties, and has been developed to encourage students todevelop skills outside their formal discipline. In support,the authors have developed two 1-credit courses specificallyfor students in the life sciences to study: (1) Biomedical Sys-tems Architectures and (2) Biomedical Systems Analysisand Design. Furthermore, the authors have developedtwo courses to allow information technology students todevelop skills in biomedical informatics. The focus of thismanuscript is limited to the development and rationale ofa formal education program in biomedical informatics spe-cifically for students majoring in information technologyprograms.

2. Student audience

One of the fundamental decisions to consider whendesigning an interdisciplinary program, in this case theintegration of life sciences with information technology,is how to most effectively augment one discipline with therelevant content from the other. In the present case, doesan educational program intend to develop courses thataugment a life sciences curriculum with information tech-nology training, or intend to develop courses in the life sci-ences for students in an information technologycurriculum? This fundamental distinction is debatable, is

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certainly not exclusive, and often reflects the strengths andneeds of a given academic setting. Presently bothapproaches exist in academia, and are evolving to meetthe strengths of the respective student audiences. Specifical-ly, there are many components of an information technol-ogy curriculum that are not required for life sciencestudents to become proficient in bioinformatics. Similarlythere are many components of a life sciences curriculumthat are not necessary for information technology studentsto be proficient in biomedical informatics. For example,students in an information technology program will devel-op skills in the bioinformatics domain by understandingthe data types (e.g., gene sequences), search query methodsand algorithms, existing databases, development and utili-zation of distributed architectures, as well as processes thatare evolving to directly integrate with common biotechnol-ogy methods (e.g., DNA microarray data collection andanalysis, automated DNA sequencer data collection andanalysis).

The distinction between life scientists developing skills ininformation technology versus information technologistsdeveloping skills in life sciences is best articulated as bioin-formatics tool users and bioinformatics tool builders,respectively. This distinction captures the rationale for con-tent in courses developed for information technology stu-dents, which are seen as both hardware and software toolbuilders. Notably, the common ground between ‘toolbuilders’ and ‘tool users’ is applications development,which is a difficult subject to address completely in bioin-formatics given the breadth of programming languages uti-lized (e.g., C++, java, perl, VB, etc.) as well as theemergence of new application development tools (e.g.,bioperl, XML, etc.), much of which already exists in infor-mation technology curricula. Furthermore, the develop-ment, implementation and utilization of bioinformaticssystems require that operational and architectural specifi-cations be developed, which form the basis for a develop-ment plan. This rationale reflects the professionalactivities in the pharmaceutical, biotechnology and bio-medical industry where an interdisciplinary team is taskedwith developing or updating the capabilities of systems inthis arena. In support, a task force within the AmericanCollege of Medical Informatics (ACMI) published theirrecommendations regarding the objectives of biomedicalinformatics training, which included the adoption of inter-disciplinary curricula, and challenged educators to providetraining in diverse fields of study (Table 1) [8]. Educatorsand professionals considering training issues and objectivesin biomedical informatics are encouraged to read this taskforce report from the ACMI [8].

Furthermore, the creation of interdisciplinary curriculaas described in this paper reflects the proposed InformationTechnology (IT) model curriculum outlined in the Associ-ation for Computing Machinery (ACM) Special InterestGroup on Information Technology Education (SIGITE)curriculum committee report dated April 2005 [9]. As youcan see from Table 1, the skills identified by the ACMI

report can be directly linked to learning outcomesaddressed in the proposed model IT curriculum. This inter-section of educational objectives provides the rationale forIT curricula strongly supporting a Biomedical Informaticsprogram.

3. Course development

The development of a course series in biomedical infor-matics for students in our information technology programspans the upper-undergraduate and graduate level. Therationalization for this level of training involves theassumption that students will apply skills developed in theirundergraduate information technology program to the bio-informatics domain. We have developed a 1-credit intro-ductory course that is offered to both graduate andundergraduate students, which provides prerequisite con-tent for the more intensive graduate-level coursework.Importantly, students do not require any prerequisite lifesciences coursework for the course series described herein.Certainly previous coursework in biology benefits the stu-dents (and typically reflects a given student’s interest in thisarea), however we have not identified an existing course inthe life sciences (to serve as a prerequisite course) that pro-vides training in, for example DNA sequence, withoutmuch more emphasis placed on molecular aspects of genet-ics that are not requisite knowledge for the biomedicalinformatics course series. As an example, our students donot need to be capable of diagramming the structures ofnucleotide and amino acids, the building blocks of DNAand proteins, to be proficient in sequence alignment queriesin DNA and protein databases.

The overarching goal of this training is to provide infor-mation technology students the ability to support team-based bioinformatics systems development and integration.The team-based aspect is paramount as information tech-nology students are not likely to initiate and lead life sci-ences-based research efforts (e.g., genomics, proteomics,cheminformatics, metabolomics, etc.), which involve inter-disciplinary teams of biologists, statisticians, and informa-tion systems specialists. Under this interdisciplinary teammodel, information systems specialists conduct require-

ments discovery by being engaged in perpetual dialoguewith biologists to understand the immediate and long-termobjectives of the project, and therefore need to understandthe methods and data structures common, and under devel-opment, in this domain. For example, using genomic toolsto research the genetic basis of cancer involves a clinicalunderstanding of cancer and effective therapies, as well asan information system to manage and query patient-specif-ic health-related data, DNA microarray data (raw dataimages, gene-specific data, single nucleotide polymorphismdata, etc.), sequence databases, and protein databases.Importantly, the first clinical manifestation of personalizedmedicine is upon us [7], where genomic methods will beemployed to identify common single nucleotide polymor-phisms (SNPs) in specific drug metabolizing enzymes for

Table 1Core skills from fields related to information and computational sciences

American College of Medical Informatics (ACMI) Report [8] Special Interest Group on Information Technology Education (SIGITE) Document [9]

Algorithms Section PF4: Algorithms and problem solvingBasic mathematics, including calculus Section 8.1.1: MathematicsCognitive/human factors and interfaces Section HCI: Human computer interactionData structures Section PF1: Fundamental data structuresDatabase design Section IM2–IM6: Database query languages, data

organization architecture, data modeling, managing the databaseenvironment, and special-purpose databases

Evaluation/research methods Section 8.1.2: Scientific methodInformation retrieval Section IM: Information managementKnowledge representation Section IM: Information managementModeling Section IM4: Data modeling and throughout the entire curriculumNetworking/architecture Section NET: Networking and IAS (information assurance and security)Ontology/vocabulary Section 8.1.3: Familiarity with application domainsProbability/statistics Section ITF6: Applications of math and statistics to ITProgramming languages Section PF: Programming fundamentals and WS Web systems and technologiesSimulation Section 6.2: Experiential learningSoftware engineering Section SIA: System integration and architecture

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the purposes of identifying persons that have a deficiency inmetabolizing (clearing) certain drugs. Although much ofthe hype regarding this subject surrounds the impact ofthe human genome sequence and genomic biotechnology,the implementation of this genetic screening system in thehealthcare industry requires the development of a support-ing, scalable information system integrated with multipletesting biotechnologies, databases, and interfaces for labtesting staff, health care providers, insurance carriers, andthe general public, not to mention the relevant informationsecurity issues in patient-specific data transfer and storage.Importantly, this system must be scalable to accommodatea growing number of patients, as well as the inevitablegrowth of genetic-based tests/information for each patient.

3.1. Course 1: introduction to computational life sciences

The first course in this series is entitled ‘‘Introduction toComputational Life Sciences.’’ This course introduces stu-dents to the central dogmas of cell biology, or more specif-ically how information flows through a cell system. Thisincludes DNA (nucleotide sequence and gene structures)as an information repository, messenger RNA as the genet-ic information retrieved and required for a specific cell sys-tem, and protein as the functional entity and outcome ofgenetic information utilization (in molecular terms; tran-scription and translation). As students grasp the informa-tion contained in the primary sequence of gene andproteins, the methods used to analyze molecular sequencesand search for sequence similarity across and between gen-omes and proteomes is studied. This proceeds into the rela-tionship between protein sequence and function, as well ashow pharmacological agents bind to, and disrupt proteinfunction, thereby introducing the students to processes invirtual drug screening. The integration with high-through-put and high-content biotechnology platforms is studied tooffer students a ‘‘state of the art’’ view of biotechnology,

which includes class sessions in a genomics laboratory(DNA microarray), proteomics laboratory (mass spec-trometry) and atomic force microscopy laboratory (cellularimaging). Finally, the course concludes with a survey ofprocesses essential to public health and clinical medicine,including emerging areas such as teleradiology and genet-ic-based personalized medicine.

The learning outcomes for the course include an under-standing of nucleotides and amino acids as building blocksfor gene and proteins, respectively. How genetic informa-tion is ‘‘transcribed’’ into an active template, ultimately‘‘translated’’ into functional proteins. The methods usedto represent nucleotides and amino acids in the digitalrealm, and how to access the enormous (publicly available)gene and protein databases available through client-serverarchitectures. The algorithms (Smith–Waterman [10], Nee-dleman–Wunsch [11]) used to efficiently carry out pair-wisesequence alignment queries (e.g., BLAST) and multiplesequence alignments (e.g., CLUSTAL). The methods andrationale for constructing cladograms to measure paralo-gous and orthologous relationships based on gene and pro-tein sequence similarity. The genetic basis of singlenucleotide polymorphisms (SNPs), existing SNP databases,and the role of SNPs in personalized medicine and foren-sics. The rationale and utilization of DNA design applica-tions supporting molecular biology methods (e.g., PCRprimer design). The rationale and utilization of 3-dimen-sional protein rendering applications. All aspects of DNAmicroarray technology including DNA probe design(gene-specific sequence analysis), sample labeling andhybridization, microarray imaging and image analysis,data analysis, and gene-specific bioinformatics (k-meansclustering, hierarchical clustering, similarity clustering,and sammon mapping). The emphasis on DNA microarraytechnology reflects the emerging utilization of thisassay platform in both basic and clinical research. Inaddition the course includes on-site demonstrations of

M.D. Kane, J.L. Brewer / Journal of Biomedical Informatics 40 (2007) 67–72 71

‘‘high-content’’ biotechnology applications, the existingdata acquisition and management systems, as well as dis-cussions with users regarding the short-comings of theexisting data acquisition and management systems. Cur-rently, we demonstrate DNA microarray technology(gene-specific data involving thousands of genes), massspectrometry (protein analysis) and atomic force microsco-py (3-dimensional detection and rendering of cellularstructures).

3.2. Course 2: biomedical informatics: systems analysis

and design

The second course in this series is entitled ‘‘BiomedicalInformatics: Systems Analysis and Design.’’ This courseapplies the skills in systems analysis and design (SAD) tothe biomedical informatics domain. More specifically, theanalysis and development of systems in this domaininvolves identifying the computational, architectural andperformance specifications of a given system or subsystemto be developed and implemented to utilize genomic and/orproteomic data in basic and applied research (e.g., healthcare, pharmaceutical research, veterinary sciences, agricul-ture, biodefense, environmental sciences, etc). Students areexposed to a survey of information systems that supportacademic, governmental and industrial information sys-tems recently developed to manage genomic and proteomicdata. These systems are dissected to reveal the rationale fortheir (often distributed) architectures, data storage require-ments, and data processing capabilities. Furthermore, thelimitations of these systems are discussed as we explorethe next generation of biomedical informatics systems. Stu-dents are assessed on their ability to perform in a teamenvironment and design dedicated systems for the manage-ment of genomic and proteomic data. For example, stu-dents are designing systems to support genome-basedpersonalized medicine, where patient-specific genetic infor-mation is derived using DNA microarray technology,screened for parameters associated with genetic risk of dis-ease, generate categorical responses and reports based onresults (e.g., warning of heightened risk of adverse drugresponse), integration with corollary health data, securelystored and accessed, and contribute to an anonymous data-base of human genetic profiles.

The objective of the course content and team-projects isto allow students to understand how genomic and proteo-mic data is utilized in a research setting, a cost-benefit anal-ysis of these systems during the systems development lifecycle, the integration with existing biotechnology platformsand data formats, as well as the financial implications forthese systems in health care and pharmaceutical research.In addition, students are expected to read and report onassigned research papers within the bioinformatics domain.The learning outcomes for this course involve the develop-ment of knowledge of bioinformatics systems performanceobjectives, the application of systems analysis and designmethods to the development of biomedical information

systems, the ability to evaluate existing biomedical infor-mation systems for limitations in scalability and integra-tion with other systems, and the ability to assemblespecifications and design an information system to utilizegenomic and/or proteomic data.

4. Programmatic analysis

It is difficult to exhaustively evaluate the educationalfoundation of bioinformatics training for the simple factthat this interdisciplinary field is very new, and new under-graduate and graduate programs are emerging each year. Itis a fair generalization that these emerging biomedicalinformatics programs have been structured to offer cross-training in both molecular biology (life sciences) and infor-mation/computer technology, with an inference that anygiven student can excel in both arenas. More specifically,it is agreed that students within the primary area of life sci-ences can be trained in various aspects of information tech-nology and computer science, yet are not expected todevelop the depth of training that is expected of IT profes-sionals and computer scientists. Certainly the contrary istrue that information/computer technology students arenot expected to garner the depth of training that is expect-ed of molecular biologists. Given the disparate, yet over-lapping nature of these professional domains, biomedicalinformatics struggles to define itself as a distinct domain,particularly when viewed from an educational/training per-spective. It is the authors’ view that as the arena of biomed-ical informatics continues to evolve, the primary influenceon its evolution (and what is expected of a ‘‘biomedicalinformatician’’) is derived from the information technologyarena and the storage, retrieval and processing capabilitiesof modern information systems. More specifically, infor-mation technology professionals that command a basicunderstanding of the data types and research objectivesof life sciences research will be tapped to develop the sys-tems to support the clinical implementation of genomic,genotyping and other high-content assay platforms. There-fore the preferred approach to training in biomedical infor-matics is to consider genomics and proteomics asapplication domains for information system developmentstudents who are familiar with the dynamic state of infor-mation technology capabilities and are trained to design,develop, and implement information systems that workwithin existing data types (e.g., FASTA sequence file for-mats) and can accommodate emerging data types from bio-technology advancements, evolving systems modeling anddevelopment languages, and the continuing evolution ofhardware and computational capabilities. It is this perspec-tive that forms the foundation of our emerging program.

Given the fact that new academic biomedical informat-ics programs are constantly emerging, the authors havechosen to compare their course development strategy totheir peer institutions in information technology education.The peer institution group includes five universities thatco-founded the Special Interest Group on Information

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Technology Education (SIGITE) organization, whichinclude Purdue University, Rochester Institute of Technol-ogy (RIT), Brigham Young University (BYU), GeorgiaSouthern University (GSU), and the University of Pennsyl-vania (PENN). All of these institutions have embarked onthe development of interdisciplinary training in biomedicalinformatics, with the exception of GSU, as of June, 2005.

Perhaps the most distinct aspect of the course seriesdescribed in this manuscript, and the intended studentaudience, is the absence of prerequisite coursework inthe life sciences. More specifically, the development ofa course series that makes no inferences to expectedtraining in molecular biology or biochemistry. Asdescribed above, the students derive an understandingof these areas as they relate to data formats and storage,integration with existing biotechnology applications (e.g.,DNA sequencing, microarray analysis) and client-serversystem architectures developed to support routinesequence analysis (e.g., BLAST). A comparison to ourpeer institutions must be qualified with the fact that theirprograms are primarily housed in the life sciences arenaand are more developed as formal programs. A simpleassessment reveals this trend as a balance of life sciencecourses versus computer/information technology (IT)courses in each of the peer institution’s highest degreeprograms in bioinformatics; MS degree at RIT (4 ITcourses, 8 non-IT courses), BS degree at BYU (6 ITcourses, 14 non-IT courses), and MS degree at PENN(2 IT courses, 6 non-IT courses). It is important to notethat this simple description of programmatic require-ments is limited to the minimum requirements reportedin published documents, and additional course hoursmay be required but the specific courses are chosen atthe student’s discretion. Purdue University has initiateda similar interdisciplinary graduate program to offercross-training for students pursuing diverse graduatedegrees, which is distinct from the emerging course seriesdescribed in this manuscript. Fundamentally the author’sefforts reflect a need for more involvement from the ITside, ultimately developing and supporting the need forimpact of biomedical informatics in the clinical arena.

5. Conclusion and future directions

The future of this course series will involve the incorpo-ration of ongoing research projects both on campus andavailable through outside partnerships. The rationale forthis ‘‘advanced’’ course is to allow students to function

within an interdisciplinary team development environment,contribute to the project, and present their contribution ata national conference. Students at this level will have dem-onstrated knowledge of this domain both through course-work and contributions to a specific project.

The employment prospects for graduates in biomedi-cal informatics are inviting, and unprecedented growthfor this interdisciplinary domain reflects the recentadvancements in genomic sequence availability, high-con-tent biotechnology screening systems, as well as theexpectations of computational biology to command aleading role in drug discovery and disease characteriza-tion. In 2004, the California Employment DevelopmentDepartment reported the top two occupations leadingemployment growth were in this interdisciplinary fieldof study. The forecasts included 99% job growth for amaster’s degree-level Bioinformatics Specialist, as wellas 59% job growth for a bachelor’s degree-level ScientificProgrammer Analyst, over the next 5 years [12].

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