Campus Visit Options

Bioinformatics: Departmental Q&A

Join us on for a virtual Q&A to learn more about our programs. Future dates to be announced.

At this online Q&A event, you will have the opportunity to:

  • Meet our DBHI faculty and learn about their research
  • Identify potential career paths and research areas
  • Get advice in light of the current job market and trends
  • Learn how UMKC’s GCCR, MSB, and IPhD programs provide a competitive edge

Can’t attend? You can access the recording of our last event online.

Biomed Grad School Fair

Biomed Virtual Grad School Fair Banner

Planning to apply to Graduate Schools of Biomedical Sciences?

You’re invited to attend the BioMed Virtual Grad School Fair

The Biomed Virtual Grad School Fair will allow you to have your admissions questions answered by representatives from graduate  institutions from across the country.

  • REGISTER now to attend the FREE virtual fair to learn more about Graduate Schools of Biomedical Science Programs
  • Save your valuable time by meeting school representatives in live chat sessions online
  • Have your questions answered without ever leaving the comfort of your computer
  • Only a one-time registration required to meet with multiple schools
  • Click HERE to register for the September 17th Fair today!
Academic Advisor Meetings

The School of Medicine encourages interested applicants to schedule a one-on-one meeting with Academic Advisor, Nick Dean to get a better sense of UMKC’s Bioinformatics program. During these meetings, prospective students are able to ask specific questions about the program, learn more about the various degree completion options, and experience the Hospital Hill Campus first hand.

To request a meeting, contact the Office of Admissions, and we would be happy to assist.


For more information on visit options or the programs offered in collaboration with the Department of Biomedical and Health Informatics, please contact the UMKC School of Medicine Office of Admissions at 816-235-1870 or medicine@umkc.edu.

Courses

MEDB 5501 – Biostatistics I
This course is the first course in the Biostatistics sequence and is intended for students, physicians, and researchers in the biological, clinical and medical fields. It will introduce statistical concepts, analysis methods, and research designs for applied data commonly     encountered in the biological, clinical and medical research. Topics include an introduction to the use of SPSS, types of data, descriptive statistics, illustrative statistics, principles in sampling, hypothesis testing, parametric analysis concepts and techniques, nonparametric analysis concepts and techniques, correlation, and linear regression. Familiarity with basic statistics is not required. Statistical analyses involved in this course will be performed primarily using the SPSS statistical analysis package. Students can opt to use SAS for analysis however SAS will not be taught in this course. The student using SAS must have a working knowledge of SAS. The course will also cover the presentation of analytical results and graphic representation of data.  (3 credit hour course)

Prerequisites: An advanced math course (i.e., Calculus, statistics).
MEDB 5502 – Biostatistics II
This course is the second course in the Biostatistics sequence and is intended for students, physicians, and researchers in the biological, clinical and medical fields, and medical education. This course introduces statistical concepts, analysis methods, and research designs for applied data commonly encountered in the biological, clinical and medical research. Topics include diagnostic testing, hypothesis testing, power analysis, analysis of variance, analysis of covariance, multivariate analysis of variance, propensity scoring, simple and multiple regression, logistic regression, and survival analysis, propensity score concepts and evidence based medicine. Familiarity with the basic statistics and statistical techniques presented in Biostatistics I is required. Statistical analyses involved in this course will be performed primarily using the SPSS statistical analysis package. The course will also cover the interpretation, presentation and the write up of analytical results and graphs. Students can opt to use SAS for analysis however SAS will not be taught in this course. The student using SAS must have a working knowledge of SAS. The course will also cover the presentation of analytical results and graphic representation of data.  (3 credit hour course)

Prerequisites: MEDB 5501.
MEDB 5503 – Biostatistics III- Mixed Model
This course is the third in the Biostatistics sequence and is intended for students, physicians and researchers in the clinical, biological, and medical fields. The course builds on foundations of linear regression model and logistic regression model. It will cover hierarchical linear mixed model for normally distributed outcomes, and generalized linear mixed models for binary outcome, count data or liker scale outcome. The course offers step by step understanding of how to construct a model with random effect. Specifically, we cover model techniques to address data resulting from cluster randomized study, repeated measures and longitudinal study. Students will learn about these different mixed models and use SAS and R to perform the analyses. The course will also cover the presentation of analytical results and graphic representation of data.  (3 credit hour course)

Prerequisites: Biostatistics I, II, and intro to SAS and R.

 

MEDB 5506 – Introduction to SPSS
A working knowledge of statistical software is a vital skill for anyone involved in quantitative research.  This class will introduce data management, simple descriptive statistics, and basic graphical display using the SPSS software package. Students will develop the fundamental skills needed to prepare datasets for analysis using SPSS. Students will conduct simple descriptive and graphic analyses and report those analyses in an appropriate and accepted manner. The class will introduce basic methods for data import, data management, simple graphics, and basic statistical analysis. This class will not cover advanced statistical methods.  (1 credit hour course)
MEDB 5507 – Introduction to SAS
Course provides a working familiarity with SAS. Students are not expected to have advanced programming or statistical analysis skills, other than the ability to create and modify text files. Basic methods for data import, data management, simple graphics, and basic statistical analysis are introduced. This class will not cover advanced statistical methods, but will provide the student with a firm foundation to address these areas in advanced statistics classes or in the student's research efforts, including thesis/dissertation research. A basic understanding of statistical terminology and a working familiarity with computer-based data files (e.g., Excel) is necessary.  (1 credit hour course)

 

MEDB 5508 – Introduction to SQL
This class is intended for researchers who need to access data stored in a relational database. It will not cover information about how to create and manage a database but rather how to use existing databases (ex: HealthFacts, EHR). You will learn how to extract data, filter, compute aggregate statistics, and join information from two different tables. The class will be taught entirely online.  (2 credit hour course)

 

MEDB 5510 – Clinical Research Methodology
Introduction to clinical research methodologies, conducts, and applications. Course provides overview of use of clinical epidemiology and bionformatics in health care.  (3 credit hour course)
MEDB 5511 – Principles & Applications of Epidemiology
This 3 credit-hour course will address the essential questions of “Who gets sick? What causes disease? And why do some groups get sick, but not others?” The course includes an overview of foundations of epidemiologic terminology and methods using examples drawn from current and historical literature. Lectures will introduce measures of disease frequency and occurrence in human populations, epidemiologic study designs and validity, concepts of causal inference, mitigation of non-causal associations, and concepts of interaction. Focused lectures will demonstrate the application of these concepts in select health and disease conditions.  (3 credit hour course)
MEDB 5512 – Clinical Trials
This course is intended for students, physicians, and researchers who work in biomedical/clinical research and wish to understand the design, logistics, interpretation, and implications of clinical trials. The course requires no specific prerequisites, though this course will apply theory from biostatistics, bioethics, research methodology, and responsible conduct of research, research design/methods, and more. Clinical trial topics included in the course are ethical considerations, trial design and phases, logistical considerations when conducting a trial, and interpretation of all aspects of a trial (including its results). The course seeks to immerse participants in the most up to date terminology and reporting guidelines and encourages active application of course material by reading high-impact and recent clinical trial publications. Students will be presented material such that they can read about (and possibly conduct) clinical trials in the future and decide for themselves whether trial design decisions and interpretations are scientifically valid.  (3 credit hour course)
MEDB 5513 – Overview of Health Services Research
Health services research (HSR) is a multidisciplinary field of investigation that examines how social factors, financing systems, organizational structures and processes, health technologies, and personal behaviors affect access to health care, the quality and cost of health care and ultimately population health and well-being. HSR includes study of individuals, families, organizations, institutions, community and populations and informs healthcare policymaking, improvements in clinical care, and the delivery of health care. In simple terms, HSR is the science that asks the following questions: What works? For whom? At what cost? And, under what circumstances?

This course will provide an overview of the US health care system and the delivery of health care in the US, covering the structure, financing, and organization of the US health care system and how health care delivery and health outcomes are evaluated and monitored. Course information will integrate essential information on the structure of the US health care system with detailed evaluation of published research and discussion of the methodologic and research challenges of HSR.  (3 credit hour course)
MEDB 5520 – Intro-Medical Informatics
Introduction to concepts of medical informatics and overview of the use of computers and information technologies in health care.  (3 credit hour course)
MEDB 5561 – Responsible Conduct of Research
Ethical standards and responsible practices are required for successful scientific research. At many steps in the research process, there may be ethical issues that need to be resolved in a thoughtful, responsible manner. This 3 credit-hour course covers the landscape of scientific integrity– both the principles and day-to-day practicalities of research ethics. Education in research ethics is integral to the preparation of future scientists. Course materials will be balanced among theory, history, current events, and applications to cases, with topic areas to include intellectual property and conflict of interest, human subjects research, use of lab animals, authorship, mentoring, peer-review, etc. *Online participation requirements, too.  (3 credit hour course)
MEDB 5573 – Statistical Consulting Practicum
This course is designed to provide students with an opportunity for statistical consulting training. Students will work on real consulting projects that were received through the Research and Statistical Consult Service. Projects may involve sample size calculation, study design, data analysis, generating statistical reports and manuscripts. Student will be able to apply their statistical knowledge and communication skills while learning how to work with other researchers.  (2-4 credit hour course)

 

COMP-SCI 470 – Introduction to Database Management Systems
Database architecture, Data independence, Schema, E-R and Relational Database modeling, Relational algebra and calculus, SQL, File organizations, Relational database design, Physical database organization, Query processing and optimization, Transaction structure and execution, Concurrency control mechanism, Database recovery, database security.  (3 credit hour course)

Prerequisites: COMP-SCI 303 (or COMP-SCI 352).
Co-requisites: COMP-SCI 431.
COMP-SCI 371 – Database Design, Implementation and Validation
This course discusses in detail all aspects of ORACLE database management systems. It covers in detail database design, implementation, and validation using ORACLE. In addition to these, it briefly covers ORACLE implementation, tuning, and implementation. The course is suitable for undergraduates and professionals alike.  (3 credit hour course)

Prerequisites: COMP-SCI 303 (or COMP-SCI 352).
COMP-SCI 5565: Introduction to Statistical Learning
Introduction to Machine Learning; Multivariate Distributions; Information Theory; Linear Algebra (Eigenanalysis); Supervised/Unsupervised Learning, Classification/Regression; Linear/Non-linear Learning; Introduction to Bayesian Learning (Bayes rule, Prior, Posterior, Maximum Likelihood); Parametric/Non-parametric Estimation. Recommended preparation: MATH 300; Familiarity with MATLAB.  (3 credit hour course)

Prerequisites: COMP-SCI 394R
COMP-SCI 5566 – Introduction to Bioinformatics
This course introduces students to the field of Bioinformatics with a focus on understanding the motivation and computer science behind existing Bioinformatic resources, as well as learning the skills to design and implement new ideas.  (3 credit hour course)

Prerequisites: COMP-SCI 303, a course or background in Biology (Genomics or Meta Models preferred).
COMP-SCI 5567 – Machine Learning in Bioinformatics
This course introduces students to the field of Machine Learning algorithms that are used in Bioinformatics, illustrated by several examples of applications to various problems.  (3 credit hour course)

Prerequisites: COMP-SCI 303, a course or background in Biology (Genomics or Meta Models preferred).
COMP-SCI 5590/ECE 5590: Supervised Learning, Machine Learning
This course covers the applications of machine learning techniques to the modern biomedical and biometric problems. In order to do so, the course will be presented in two parts. For the first part, a quick coverage of the most important pattern recognition and machine learning topics will be given. The second part will consist of select case studies and will be project-based. Students will be presented with a number of biomedical and biometric research problems and will be guided to find solutions to the assignments using the knowledge and the skills garnered during the first part of the class, as well as additional reading assignments.  (3 credit hour course)

Prerequisites: Either an intermediate knowledge of multivariate calculus, probability, and linear algebra with instructor’s consent, or 5590CI. Basic MATLAB programming skills are required.
LS-CBB 5530 – Cell and Molecular Biology I
Molecular aspects of gene structure and function in prokaryotic and enkaryotic organisms and their viruses. Emphasis in genome structure and organization and regulation of gene expression.  (3 credit hour course)
Co-requisites: LS-MBB 5561.
LS-CBB 5520 – Cell and Molecular Biology II
A presentation of the cellular and subcellular organization and function of eukaryotic cells. Discussions will emphasize basic concepts by which structure and functions are integrated.  (3 credit hour course)
Co-requisites: LS-MBB 5561, LS-MBB 5562.
LS-MBB 5561 – General Biochemistry I
The first semester of a two-semester sequence in general biochemistry. This course will emphasize the structure of biological molecules, thermodynamics and kinetics of biological reactions, and selected aspects of energy metabolism and metabolic pathways.  (3 credit hour course)
Prerequisites: CHEM 322R.
LS-MBB 5562 – General Biochemistry II
The second semester of a two-semester sequence in general biochemistry. This course will emphasize selected aspects of the biochemistry of metabolism and macromolecular assemblies. The molecular basis of genetic and metabolic regulation will be discussed.  (3 credit hour course)
Prerequisites: LS-MBB 5561.
BIOLOGY 5519 – Principles of Evolution
Synthesis of the modern concepts of evolution. Discussion of the biological processes that produce organic diversity through phyletic change. Discussed are variation, mutation, population genetics, natural selection and adaptation.  (3 credit hour course)
Prerequisites: BIOLOGY 206.
BIOLOGY 5525 – Bioinformatics and Data Analysis
Methods and procedures for the storage, retrieval and analysis of information in biomolecular and biological databases. Emphasis will be given to the use of database information in biological research and to recent developments in genomics and proteomics.  (3 credit hour course)
Prerequisites: LS-BIOC 341, LS-BIOC 360.

Applying for Admission

In light of COVID-19 restrictions that may impact standardized testing and other application requirements, the Office of Admissions is willing to work with current and prospective applicants to ensure consideration for the Fall 2020 semester.

Applicants for admission to the Master of Science in Bioinformatics program must submit all applications and application materials by the appropriate deadline in order to be considered for admission. Applications that become complete after the deadline will be reviewed on a space-available basis. Incomplete applications or applications started after the deadline will not be considered for admission.

Application Deadlines

Fall semester:  U.S. citizens or permanent residents – August 15

Fall semester:  International applicants – July 15

Spring semester:  U.S. citizens or permanent residents – January 15

Spring semester:  International applicants – December 15

Students applying to the Master of Science in Bioinformatics program must submit the following:

*The TOEFL exam or IELTS exam is required for international applicants whose native language is not English. Applicants who are currently participating in or have successfully completed a graduate medical residency or fellowship and have passed USMLE Step II – Clinical Skills are not required to submit a TOEFL or IELTS. On the TOEFL, and a minimum score of 550 (paper-based exam), 213 (computer-based exam) or 79 (Internet-based exam) is required. On the IELTS, a minimum score of 6.0 is required for graduate level study at UMKC.

Application Process

Step 1: UMKC General Application for Admission

U.S. citizens and permanent residents must complete the UMKC General Application for Admission.  Please follow the instructions provided on the application.

International applicants should complete the UMKC General Application for Admission and review the International Student Affairs Office website for additional information.

The following will be submitted online with the UMKC General Application for Admission:

One-Page Goal Statement

All applicants must submit a one-page goal statement explaining their interested in the Master of Science in Bioinformatics program.

Letters of Recommendation

A minimum of two letters of recommendation are required from individuals such as professors, advisors, administrators or employers who can speak to your academic ability and other personal characteristics. Family members and/or friends of the family should not provide letters of recommendation. Letters of recommendation are considered confidential material at the UMKC School of Medicine; information provided in letters of recommendation will not be shared with the applicant. The applicant is required to submit the names and email addresses of those providing the letters of recommendation.

Resume or Curriculum Vitae

All applicants must submit a current CV or Resume. The CV or Resume should minimally include your current address, employment history, and prior education.

Step 2: Transcripts & Test Scores

All applicants are required to submit official college/university transcripts to the university.  Transcripts may be sent to:

University of Missouri – Kansas City
Office of Admissions – Processing
5115 Oak Street
Kansas City, MO 64112

International applicants should submit transcripts to:

University of Missouri-Kansas City
International Student Affairs Office
Student Success Center
5000 Holmes Road, G-04
Kansas City, MO  64110

TOEFL scores are required for international applicants whose native language is not English and should be submitted electronically by identifying UMKC as a score recipient.  The UMKC TOEFL code is also 6872.

IELTS scores should be sent to the International Student Affairs Office at the address listed above.

Clinical Faculty & Fellows Research Scholarship Program

The Department of Biomedical and Health Informatics (IMPH) has established a scholarship program for clinical fellows and faculty who wish to expand their research skills by taking courses in the Master Program for Bioinformatics. The scholarship program will be used to pay tuition for selected students so that financial issues do not present a barrier to taking courses in the MS program.

Because funds are limited, applicants for the scholarship program will be ranked by the following criteria:

  1. The applicant has the potential to make a long-term contribution to UMKC.
  2. The applicant has a focus on disparities research and is committed to working with underserved and disenfranchised population groups.
  3. The program is funded by the UMKC Department of Medicine, thus in certain cases applicants from the Medicine department and other closely allied departments may be given priority. Because of the employee discount, UMKC students eligible for this program may be given first priority.
  4. For fellows, the director of the fellowship program must supply a letter agreeing to release time to take courses and at least 4 hours of study time per week. A potential clinical mentor must be identified from the affiliate or regular faculty for IMPH.  In certain cases non-affiliated faculty may be considered as appropriate mentors.
How to apply?
  1. Secure a letter of recommendation from your fellowship director.
  2. Identify your desired mentor from the faculty list. Secure their agreement.
  3. Write a one-page goal statement about why you believe this training will help you further your research career.
  4. Email the application to Cynthia Ginn MBA, ginnc@umkc.edu.
  5. Should you enroll in a course and not finish (past the official deadline for dropping) you will be required to refund your tuition to the program.
Who will select the successful scholarship applicants?

A team composed of Drs. Dev Maulik, Jill Moormeier, George Reisz, and Steve Simon will meet twice per year in November and June to select scholarship recipients.

Genomics Emphasis Area

genomics-lab The Genomics Emphasis Area emphasizes the use of existing software for biological analysis and the analysis of a diverse set of biological data.

Who are our students?

Students in the Genomics Emphasis Area are interested in learning to use and develop informatic tools to answer primary, hypothesis-driven research questions in the biological sciences. They generally have a very strong biology background married to experience with computer systems, including database design/administration and/or computer programming. Our students are motivated by research interests to stretch the boundaries of current computational tools for biology, and want to understand the assumptions that underlie current algorithms for analyzing data in fields such as genomics, proteomics, structural biology, and biochemistry.

What do our students do when they graduate?

Upon completion of the Genomics Emphasis Area our students should be attractive candidates for jobs in industry, leading teams to produce informatic solutions to biological research problems. In addition, they may seek positions in academics pursuing the development of computational tools and shaping hypothesis driven research. Their skills will be in demand when people who are primarily biologists need to interact and communicate with computer scientists.

All students must take four core courses:

Students in the Genomics Emphasis Area must also take these seven courses:

  • Cell and Molecular Biology I (LSCBB 5530)
  • Biochemistry I (LSMBB 5561)
  • Cell and Molecular Biology II (LSCBB 5520)
  • Biochemistry II (LSMBB 5562)
  • Evolution (BIOL 5519)
  • Database Design/Management (CS 470 or 471)
  • Bioinformatics and Data Analysis (BIOL 5525)

Students who have already met the requirements of some of the courses listed above may petition to substitute an elective before taking the course. Students should work with their advisor to determine the most appropriate electives. A list of suggested electives is below:

  • Mammalian Physiology (BIOL 5534)
  • Neurobiology (BIOL 5542)
  • Graduate Biophysical Principles (LSCBB 5501)
  • Graduate Virology (LSCBB 5504)
  • Membrane Biochemistry and Biophysics (LSCBB 5566)
  • Developmental Biology (LSMBB 5509)
  • Structure and Function of Proteins (LSMBB 5565)

Students may suggest additional electives.

Students in the Genomics Emphasis Area must complete a six credit hour thesis.

Computational Emphasis Area

The Computational Emphasis specializes inthe development and use of the next generation of bioinformatics tools and software.

Program Objectives

Graduates will be effective members of multi-disciplinary teams in industry or academia.

Some of the graduates will engage in advanced studies in informatics.

Learning Outcomes:

Graduates of the Computational  Emphasis will have the skills to use and develop bioinformatic tools and resources.

All students must take four core courses:

 

Students in the Computational Emphasis Area must also take these four courses:

    • Database Design/Management (CS 470 or 471)
    • Introduction to Bioinformatics (CS 5566)
    • Medical Informatics (MEDB 5120), and
    • Machine Learning in Bioinformatics (CS 5567).

Students can elect to do a 6 credit hour thesis or a 3 credit hour capstone plus one additional elective

All students should take two electives if they are selecting the thesis option and three if they are selecting the practicum option. Students should work with their advisor to determine the most appropriate electives. A list of suggested electives is below:

    • Advanced Software Engineering (CS5551)
    • Knowledge Discovery & Management (CS5560)
    • Foundations of Computational Intelligence (CS5590CI)
    • Neural and Adaptive Systems (ECE5590NN)
    • Advanced Biomedical Signal Processing (ECE5590BP)
    • Biomedical Image Processing (ECE5590B)
    • Pattern Recognition (ECE5590PR)
    • Design and Analysis of Algorithms (CS5592)
    • Architecture of Database Management Systems (CS570)
    • Large Scale XML Data Management (CS5590LD)
    • Clinical Epidemiology (BMED5111)
    • Evolution (BIOL5519)
    • Bioinformatics and Data Analysis (BIOL5525)
    • Clinical Bioinformatics (MEDB 5521)

If students have already met the requirements of a course listed above, they may petition to substitute an elective for this course.

Clinical Research Emphasis Area

Clinical_Research

The Clinical Research Emphasis of the Bioinformatics Degree Program trains aspiring researchers who want to play key roles in research as part of a larger interdisciplinary team. Our graduates will become effective team members who answer important public health questions and who improve the quality of medical care through multidisciplinary approaches to education, research, and development in the field of clinical research.

The Clinical Research Emphasis places a high emphasis on understanding how hypotheses are formulated into researchable questions, how clinical studies are designed and implemented, how to best capture the information generated by patient care and clinical studies, and the statistical methodology needed for clinical research and improved bedside care.

Professors teaching courses within this curriculum will utilize “real world” examples (programs and data sets from their own research or that of others) as a way of helping students understand the questions which are being asked and how their own skills can be developed to help answer those questions.

Students across the three emphasis areas within the Bioinformatics Program (Clinical Research, Genomics, Computational) will interact in a number of ways during the course of their studies for the Master degree. This interaction should foster the ability of the students to work together in a collaborative environment in their future research.

  • Outcomes research, utilization and costs of care
  • Public health and health promotion
  • The local to international continuum of health services
  • Outreach to special populations (e.g. refugees, Medicaid enrollees, minorities)
  • Program evaluation, characteristics and behavior of practitioners
  • AIDS and other emerging infections
  • Use of complementary and alternative medicine
  • Pharmacoepidemiology

Program Objectives:
  • We train highly competent practitioners, teachers, and researchers in the science of clinical research.
  • We train students in the creation and understanding of data generated by patient care and clinical studies and on the statistical methodology needed for clinical research and improved bedside care.
  • We apply scientific findings to clinical settings, evaluate outcomes and develop public health policy.
  • We advance research in the biostatistics, public health sciences, health services delivery, preventive medicine and public health practice.

Learning Outcomes

Graduates of the Clinical Research Emphasis Area in the Bioinformatics Master Degree Program will be prepared to:

    • Pose a researchable question, develop hypotheses, collect evidence, analyze data, and address hypotheses.
    • Design and implement cost-effective clinical trials to answer important clinical questions.
    • Access information through the use of databases and other electronic media.
    • Organize complex quantitative and qualitative information and utilize it effectively.
    • Draw conclusions after weighing evidence, facts and ideas.
    • Convey findings through written and oral communication.
    • Identify the researcher’s responsibilities in the ethical conduct of clinical research.
    • Integrate theory and research into advanced clinical practice.

 

All bioinformatics MS students must take four core courses:

 

Students in the Clinical Research Emphasis must also take these four courses:

Students in the Clinical Research Emphasis Area can elect to do a 6 credit hour thesis or a 3 credit hour capstone project plus one additional elective. However, the thesis option is strongly recommended.

All students should take two electives if they are selecting the thesis option and three if they are selecting the capstone option. Students should work with their advisor to determine the most appropriate electives. A list of suggested electives is below:

    • Qualitative Methods In Nursing Research (NURSE 5557)
    • Qualitative Research Methods (NURSE 5670)
    • Project Management (DSOM 5543)
    • Human Genome Epidemiology (MEDB 5114)
    • Special Problems in Bioinformatics(MEDB 5130)
    • Health Outcomes Seminar (MEDB 5150)
    • Medical Decision Making (MEDB 5160)
    • Clinical Bioinformatics (MEDB 5521)

“Information is as critical to the provision of safe health care – care that is free from errors of both commission and omission – as it is to the safe operation of an aircraft.”

-Institute of Medicine Committee on Data Standards for Patient Safety
2004.

Curriculum

Clinical Research Emphasis
MEDB 5501 Applied Biostatistics I 3
MEDB 5502 Applied Biostatistics II 3
MEDB 5510 Clinical Research Methodology 3
MEDB 5511 Principles and Applications of Epidemiology 3
MEDB 5512 Clinical Trials 3
MEDB 5513 Overview of Health Services Research 3
MEDB 5520 Introduction to Medical Informatics 3
MEDB 5561 Responsible Conduct of Research 3
Required Electives 6 credit hours required if choose thesis; 9 credit hours required if choose capstone 6 or 9
Required Thesis or Capstone 6 credit hours required for thesis research and preparation; 3 credit hours required for capstone research and preparation 6 or 3

 

Computational Emphasis
MEDB 5501 Applied Biostatistics I 3
MEDB 5502 Applied Biostatistics II 3
MEDB 5510 Clinical Research Methodology 3
MEDB 5520 Introduction to Medical Informatics 3
COMP-SCI 470
or COMP-SCI 371
Introduction to Database Management Systems (or)
Database Design, Implementation and Validation
3
BIOL 5525
or
COMP-SCI 5590NN/ECE 5316
or
COMP-SCI 5590/ECE 5590

Bioinformatics and Data Analysis (or)

Artificial Neural/Adaptive Systems (or)

Supervised Learning; Machine Learning

3
COMP-SCI 5565 Introduction to Statistical Learning 3
Required Electives 6 credit hours required if choose thesis; 9 credit hours required if choose capstone 6 or 9
Required Thesis or Capstone 6 credit hours required for thesis research and preparation; 3 credit hours required for capstone research and preparation 6 or 3

 

Individualized Informatics Emphasis
MEDB 5501 Applied Biostatistics I 3
MEDB 5502 Applied Biostatistics II 3
MEDB 5510 Clinical Research Methodology 3
MEDB 5520 Introduction to Medical Informatics 3
MEDB 5521 Clinical Bioinformatics 3
MEDB 5561 Responsible Conduct of Research 3
COMP-SCI 5566 Introduction to Bioinformatics 3
Required Electives 12 credit hours required if choose thesis; 15 credit hours required if choose capstone 12 or 15
Required Thesis or Capstone 6 credit hours required for thesis research and preparation; 3 credit hours required for capstone research and preparation 6 or 3
Genomic Emphasis
MEDB 5501 Applied Biostatistics I 3
MEDB 5502 Applied Biostatistics II 3
MEDB 5510 Clinical Research Methodology 3
MEDB 5561 Responsible Conduct of Research 3
LS-CBB 5530 Cell and Molecular Biology I 3
LS-CBB 5520 Cell and Molecular Biology II 3
LS-MBB 5561 General Biochemistry I 3
LS-MBB 5562 General Biochemistry II 3
BIOLOGY 5519 Principles of Evolution 3
BIOLOGY 5525 Bioinformatics and Data Analysis 3
COMP-SCI 470
or COMP-SCI 371
Introduction to Database Management Systems (or)
Database Design, Implementation and Validation
3
Required Thesis 6 credit hours required for thesis research and preparation 6

 

Practicum Option

Students in the Computational Emphasis Area or Clinical Research Emphasis Area will need to complete either a 6 hour thesis or a 3 hour practicum plus an additional elective. Students choosing to complete a practicum will need to work with a faculty mentor. A single page description of the scope of the practicum should be approved and signed by the faculty mentor and filed in the office prior to registering for the practicum. A practicum will typically require effort equivalent to a 3 credit hour didactic course. The work should be a well-circumscribed component of a research project, an in-depth review of literature on a well-defined topic, or a single semester internship in informatics. Students will be required to give a departmental presentation on their practicum at the end of the semester.