I. FACULTY

Chen, Jake, Professor of Genetics (primary) and Computer and Information Sciences (secondary), Associate Director and Chief Bioinformatics Officer of the Informatics Institute. 1995, B.S. (Peking University-China); 1997, M.S. (University of Minnesota); 2001, Ph.D. (University of Minnesota). Expertise in biological data mining, systems biology and pharmacology, and translational bioinformatics applications.

Chong, Zechen, Assistant Professor of Genetics (primary) and Informatics Institute (home). 2007, B.E. (Harbin Institute of Technology, China); 2013, Ph.D. (Beijing Institute of Genomics, Chinese Academy of Sciences-China). Expertise in Next- or Third Generation Sequence data analysis and algorithm design, genomic rearrangements, cancer genomics, epigenomics, evolution, cloud computing. 

Cimino, James, Professor of Medicine (primary) and Director of the Informatics Institute (home). 1977, B.S. (Brown University); 1981 MD (New York Medical College), 1984 Residency in Internal Medicine (St. Vincent’s Hospital , NewYork), 1988 Biomedical Informatics Fellowship (Harvard University, Massachusetts General Hospital); Particular expertise in biomedical ontologies, clinical information systems, clinical research information systems and clinical decision support.

Min, Gao, Bioinformatics Scientist at the Informatics Institute. 2007, B.S. (Shandong Normal University, China); 2013, Ph.D. (Beijing Institute of Genomics, University of Chinese Academy of Sciences, China). Expertise in NGS data analysis (whole genome sequencing, whole exome sequencing, RNA-seq, ChIP-seq, etc.), single cell genomics (single cell DNA-seq, RNA-seq, exome sequencing, bisulfite sequencing, etc), epigenomics, pipeline development.

Osborne, John D., Assistant Professor of General Medicine (primary) and Informatics Institute (home). B.S., M.S (University of Alberta), B.S. (University of Victoria); 2016, Ph.D. (University of Alabama at Birmingham). Expertise in Natural language processing (NLP), with a focus on extraction of information from biomedical text. Development of ontologies and machine learning algorithms, with practical applications in clinical informatics, genomics and translational science.

Rosenberg, Alexander, Associate Professor of Microbiology (primary) and Informatics Institute (home). 1987, B.E.E. (University of Delaware); 1993, Ph.D. (University of Pittsburgh). Expertise in Bioinformatics, NGS B cell repertoire analytics, transcriptomics, flow cytometry, data management, integration & tool development, visualization of complex biological data sets.

II. Minimum Course Credit Requirements

The UAB Graduate School has minimum course credit requirements for students in doctoral programs. Program requirements for course work may exceed the Graduate School minimum but may not be less than the Graduate School minimum. In addition, the Vice President for Research requires that all students engaging in research complete the applicable Responsible Conduct in Research requirements which can be found here. 

If entering with a baccalaureate degree, a student is required to earn a minimum of 72 credit hours consisting of the following:

1. Completion of 48 semester hours of coursework prior to candidacy:
  • A minimum of 22 hours of core coursework directly related to teh discipline
  • No more than 16 hours of non-dissertation research (i.e. 798) can be counted
  • No more tehan 10 hours of labs, seminars, directed study dedits, or GRD and CIRTL courses can be counted 000

2. Completion of 24 semester hours of research-based work over a minimum of two semesters in candidacy which can be designated as either:
  • A minimum of 24 semester hours in 799 dissertation research OR
  • A minimum of 12 semester hours in 799 dissertation research AND, either during or before candidacy, 12 semester hours in other appropriate research-based coursework which has been approved by the graduate student's program

If entering with a previously earned master’s degree appropriate to the doctoral degree field, a student is required to earn a minimum of 51 credit hours comprising the following. These requirements also apply to students with previously earned M.S., D.V.M., D.M.D., D.D.S., etc.:


1. Completion of 27 semester hours of coursework prior to candidacy:
  • A minimum of 15 hours of core coursework directly related tot he discipline
  • No more than 6 hours of non-dissertation research (i.e. 798) can be counted
  • No more 6 hours of labs, seminars, directed study credits, or GRD and CIRTL courses can be counted 

2. Completion of 24 semester hours of research-based work over a minimum of two semesters in candidacy which can be designated as either:
  • A minimum of 24  minimum of 24 semester hours in 799 dissertation research OR 
  • A minimum of 12 semester hours in 799 dissertation research AND, either during or before candidacy, 12 semester hours in other appropriate research-based coursework which has been approved by the graduate student’s program

Up to 12 credits of course work that have not been applied toward meeting the requirements for an earned degree taken at UAB or other institutions may be used to satisfy these course credit requirements upon approval of the graduate program director and the Graduate School Dean. Courses which have been previously applied toward meeting the requirements of another degree are not eligible to satisfy minimum course credit requirements. The student’s graduate department or program should provide a course planning curriculum worksheet along with the student’s application for degree. This worksheet should detail the courses taken which are intended to be used toward meeting degree requirements.

III. Sample Program of Study

Year(Summer Jun - Jul)Fall Semester (Aug - Dec)Spring Semester (Jan - May)
Year 1 INFO 510*

GBS 795 Lab Rotation
GBS 707
GBS 708
GBS 709
INFO 601
GBS 795/796 Lab Rotation
INFO 602
INFO 603
INFO/BST 621 (to replace GBSC 722)
GBS 796/797 Lab Rotation
       
       
Year 2   INFO 604
INFO/NUR 690
INFO 691
GRD 717
INFO 692
INFO 693/793
       
       
Year 3   INFO 691
INFO 693/793
GBS 725 GGB Grant Writing
INFO 692
Deadline to complete all electives; Deadline to pass written and oral qualifying exam
       
       
Year 4   INFO 691 INFO 692
       
       
Year 5     Must complete 18 hr of Thesis Dissertation to graduate
       
       
year 6     Deadline to complete thesis defense
       

All courses: 72 hours

  • GBS Core Courses: (GBS 707, GBS 708, GBS 709, GRD 717, GBS 725) = 9 hours
  • Lab Rotation: (795, 796) = 2 hours
  • INFO core: (601, 602, 603, 604, 621) = 15 hours
  • GGB Module: any 2-hr module = 2 hours
  • Electives: 10 hours (at least 3 hours from major elective)



* Remedial Courses do not count towards degree requirement.

IV. Core (Required) Courses

Remedial Courses for Bioinformatics Majors

INFO 501                   Biomedical Informatics Research                                3 hours


This course provides an overview of the field of biomedical informatics, including subfields ranging from bioinformatics to public health informatics, from the perspective of research accomplishments and challenges. Each topic will be taken from a historical perspective – where are we now and how did we get here – and then explore the current research directions. There will be an emphasis on underlying concepts, theories, and methods.   Although this course can serve as a survey of the field, it is also intended for students who will pursue research in some area of biomedical informatics.

INFO 510                   Bioinformatics Application Skills                                    2 hours

This course provides students necessary bioinformatics programming and data skills using Linux, MySQL and R. Linux commands and use of scripting languages will be taught in the context of bioinformatics data processing. Basic and practical database skills will be covered. Basic statistics using R to conduct reproducible research will be taught. Students will learn homology search using BLAST, understand basic next-generation sequencing data processing and analysis pipeline development. The focus will be on practical bioinformatics concepts using scripting/programming applied to data analysis problems

Core GBS Courses - PhD Only

GBS 707                     Basic Biochemistry and Metabolism                              2 Hours

This course is intended to provide students a rigorous background in the principles of biological chemistry. The principles taught are those we believe student should master and include the application of these principles to research protocols and performance. Must be admitted into one of the Graduate Biomedical Sciences (GBS) Themes. Required of all first year GBS students.

GBS 708                     Basic Genetics and Molecular Biology                           2 Hours

This course is intended to provide students with a strong foundation in basic genetics and basic molecular biology so that students are able to apply and understand fundamentals in their lab research. Must be admitted into one of the Graduate Biomedical Sciences (GBS) Themes. Required of all first year GBS students.

GBS 709                     Basic Biological Organization                                         2 Hours

This course is intended to provide students with exposure to the fundamentals of basic cell biology and begin to build a foundation of knowledge that will be needed as the student progress along the scientific path. Must be admitted into one of the Graduate Biomedical Sciences (GBS) Themes. Required of GBS first year students.

GBS 725                     GGB Grant Writing                                                         2 Hours

The objective of the course is to teach students how to effectively write grant proposals. This course will provide hands on training in the preparation of a grant application and demonstrate effective strategies for assembling a successful proposal. With guidance from the faculty, the students will write a NIH style proposal on their dissertation research topic. After the proposal is complete, each grant will be reviewed in a mock NIH study section. Based on the comments from the study section, the student will revise the application and submit the proposal to his/her thesis committee as part of the qualifying examination for admittance into candidacy.

GBS 795                     Lab Rotation 1                                                               1-5 Hours

First rotation for first year GBS Theme students.

GBS 796                     Lab Rotation 2                                                               1-5 Hours

Second rotation for first year GBS Theme students.

GBS 797                     Lab Rotation 3                                                               1-9 Hours

Third lab rotation for first year GBS theme students.

Core Graduate Courses – PhD Only

GRD 717                    Principles of Scientific Integrity                                     3 Hours

The Principles of Scientific Ethics course (GRD 717) includes a blended approach of on-line training and in-person discussion on topics related to the responsible conduct of research (RCR). Specifically, the on-line training component includes completion of all RCR-related CITI Program modules; participants are required to successfully complete each of these modules, achieving a score of 80% or better. Once completed, participants then attend an in-person discussion session that consists of an all-day Saturday workshop facilitated by training program directors, preceptors, and administrators. Three Saturday sessions are offered so that participants and facilitators have the opportunity to select a date that best fits their schedules. These sessions debate case-studies in a team-based learning format as well as allow for additional RCR-related activities, such as panel discussions with faculty and administrators regarding ‘real-world’ RCR examples and role-playing RCR scenarios.

Core courses for Bioinformatics Majors

INFO 601/301            Introduction to Bioinformatics                                        3 hours
INFO 701

Introduction to bioinformatics and computational biology, with emphasis on concepts and application of informatics tools to molecular biology. It covers biological sequence analysis, gene prediction, genome annotation, gene expression analysis, protein structure prediction, evolutionary biology and comparative genomics, bioinformatics databases, cloud computing, basic R-based data analysis, simple programming skills using Perl, Linux/Unix environment and command lines, visual analytics, and social/legal aspects of open science. It will have a class research project component.

INFO 602                   Algorithms in Bioinformatics                                           3 hours
INFO 702

This course introduces various fundamental algorithms and computational concepts for solving questions in bioinformatics and functional genomics. These include graph algorithms, dynamic programming, combinatorial algorithms, randomized algorithms, pattern matching, classification and clustering algorithms, hidden Markov models and more. Each concept will be introduced in the context of a concrete biological or genomic application. A broad range of topics will be covered, ranging from gene identification, genome reconstruction, microarray data analysis, phylogeny reconstruction, sequence alignments, to variant detection. Pre-requisite is INFO 701 or with instructor permission.

INFO 603                   Biological Data Management                                           3 hours
INFO 703

The introduction of biological data management concepts, theories, and applications. Basic concepts such as relational data representation, relational database modeling, and relational database queries will be introduced in the context of SQL and relational algebra. Advanced concepts including ontology representation and database development workflow will be introduced. Emerging big data concepts and tools, including Hadoop and NoSQL, will be introduced in the context of managing semi-structured and unstructured data. Application of biological data management in biology will be covered using case studies of high-impact widely used biological databases. A class project will be required of all participants. Pre-requisite is INFO 601.

INFO 604                   Next-generation Sequencing Data Analysis                 (3 hours)
INFO 704

The introduction of next-generation sequencing (NGS) technologies and the various new genomics applications. Basic analysis begins with NGS data representations using FASTQ, BAM, and VCF files. Major NGS applications in the characterization of DNA, RNA, methylation, ChIP, and chromatin structure analysis will be described. Topics will cover alignment, whole genome de novo assembly, variant detection, third generation sequencing technologies, functional genomics, metagenomics, single cell genomics, genetic diseases and cancer genomics. NGS workflows and translational applications in disease biology and genome medicine will also be emphasized. Pre-requisite is INFO 601.

INFO 611                   Intermediate Statistical Analysis I                                (3 hours)
INFO 711
Cross listed to BST 621

Students will gain a thorough understanding of basic analysis methods, elementary concepts, statistical models and applications of probability, commonly used sampling distributions, parametric and non-parametric one and two sample tests, confidence intervals, applications of analysis of two-way contingency table data, simple linear regression, and simple analysis of variance. Students are taught to conduct the relevant analysis using current software such as the Statistical Analysis System (SAS).

INFO 690                   Data Mining and Statistical Learning                          (3 hours)
INFO 790
Cross listed to NUR 790

Students will learn to discover and implement meaningful insights and knowledge from data. This course covers major concepts and algorithms of data mining. The course will be taught using the SAS Enterprise Miner program. The final project will demonstrate all the data mining techniques covered in the course and furthermore expose students working with real data. At the end of the course students will be proficient in utilizing data mining techniques to exploit data patterns and behavior, gain insider understanding of the data, and produce new knowledge that healthcare decision-makers can act upon. Furthermore, SAS Certified Predictive Modeler certification exam will be offered at the end of the course. Instructor permission is required.

INFO 691                   Bioinformatics Seminar I                                                (1 hour)
INFO 791

For master’s student only. Students will learn how to prepare, present, and critique research presentations in bioinformatics by attending seminar presentations made by presenters. Seminars are presented by graduate students, faculty, visitors, or online speakers. Students must show evidence of prior preparation, active participation, and documented comprehension of the topics. Pre-requisite is INFO 601.

INFO 692                   Bioinformatics Seminar II                                               (1 hour)
INFO 792

For master’s student only. Students will learn how to prepare, present, and critique research presentations in bioinformatics by attending seminar presentations made by presenters. Seminars are presented by graduate students, faculty, visitors, or online speakers. Students must show evidence of prior preparation, active participation, and documented comprehension of the topics. Pre-requisite is INFO 691/INFO 791.

INFO 693                   Bioinformatics Journal Club                                         (2 hours)
INFO 793

Students will learn how to read, present, and critique primary research publications in bioinformatics. Journal club participants will present high-impact recent journal publications selected by course instructors and learn how to read the paper, write critiques, and organize analysis insights into review papers. Students must show evidence of prior preparation prior to journal clubs and write critiques to show comprehension of the topics throughout the semester. Pre-requisite is INFO 691/INFO 791.

Core – MS Only

INFO 698                   Bioinformatics Master’s Projects                                 1-6 hours

Admission to bioinformatics master’s program (Plan B: “Project Option”) is required. Independent study to conduct bioinformatics research projects, guided by the instructor as the mentor. Permission of instructor and graduate program director is required.

INFO 699                   Bioinformatics Master’s Thesis Research                   1-6 hours

Admission to bioinformatics master’s program (Plan A: “Thesis Option”) is required.

Core – PhD Only

INFO 794                   Advanced Bioinformatics Journal Club                         2 hours

Students will learn how to read, present, and critique primary research publications in bioinformatics. Journal club participants will present high-impact recent journal publications selected by course instructors and learn how to read the paper, write critiques, and organize analysis insights into review papers. Students must show evidence of prior preparation prior to journal clubs and write critiques to show comprehension of the topics throughout the semester. Pre-requisite is INFO 693.

INFO 799                   Bioinformatics Research for Dissertation                 1-12 hours

Admission to candidacy is required

V. Informatics Major Electives

INFO 671                   Clinical Informatics Seminar I                                          1 hour

For master’s student only. Students will learn how to prepare, present, and critique research presentations in clinical informatics by attending seminar presentations made by presenters. Seminars are presented by graduate students, faculty, visitors, or online speakers. Students must show evidence of prior preparation, active participation, and documented comprehension of the topics. Pre-requisite is INFO 501.

INFO 672                   Clinical Informatics Seminar II                                        1 hour

For master’s student only. Students will learn how to prepare, present, and critique research presentations in clinical informatics by attending seminar presentations made by presenters. Seminars are presented by graduate students, faculty, visitors, or online speakers. Students must show evidence of prior preparation, active participation, and documented comprehension of the topics. Pre-requisite is INFO 671.

INFO 673                   Clinical Informatics Journal Club                                    1 hour
Cross listed to GBSC 700-VTQ

Biomedical informatics is an interdisciplinary field that brings together biology, medicine, nursing, computer science, cognitive science, public health and much more. This journal club will discuss papers across the spectrum of bioinformatics, translational research informatics, clinical research informatics, clinical informatics and population informatics. Presenters will be expected to discuss papers approved by the instructor, with discussion of historical context of the work, comparison with similar papers, and critique of the science presented.

INFO 712                   Visual Analytics for Biomedical Research                      3 hours
INFO 612

In this course, we will explore the use of visualization techniques as a concise and effective way to help understand, interpret and communicate complex biological data. Principles of design, visual rhetoric/communication, and appropriate usage will be introduced. We will cover representation of different data types, concentrating on those generated by data-rich platforms such as next-generation sequencing applications, cytometry, and proteomics, and will discuss the use of visualization techniques applied to assessing data quality and troubleshooting. Various topics including dimension reduction, hierarchical visualizations, unsupervised learning, graph theory, networks/layouts and interactivity will be covered. We will review the algorithmic underpinnings of various methods that lead to their appropriate and effective use. Finally, we will review a variety of genomics/bioinformatics-related visualization tools that are available online, and will explore the use of lower-level approaches (like Matlab or R) to create beautiful and effective visualizations. Pre-requisite is INFO 703 (INFO 603/703 - Biological Data Management) or permission of the instructor.

INFO 751                   Systems Biomedicine of Human Microbiota                  3 hours
INFO 651

The human microbiota is the collection of microorganisms (bacteria, archaea, fungi and viruses) that reside within human tissues and biofluids. Such resident microorganisms compose the majority of cells in human bodies and are key contributors to human development, health, and disease. However, most studies focus on genomics and microbiome statistical representations alone, while spatial-temporal analysis, multi-source data integration and modeling are necessary to predict and understand interactions between microorganisms, human hosts, and the environment. This course will highlight state-of-the-art microbiome/microbiota research and provide essential training in mathematical, computational and systems biology to derive integrative and predictive models of microbiota-host interactions in the context of human health and disease. Pre-requisite is INFO 601 and one of [MA 560, BME670] or permission of the instructor.

INFO 762                     Biomedical Applications of Natural Language Processing     3 hours
INFO 662
Cross listed to CS 662

Students will be introduced to Natural Language Processing (NLP) including core linguistic tasks such as tokenization, lemmatization/stemming, POS tagging, parsing and chunking. Applications covered include Named Entity Recognition, semantic role labeling, word sense disambiguation, normalization, information retrieval, question answering and text classification. Applications and data will have a biomedical focus, but no biology or medical background is required. Pre-requisite are: INFO 701/INFO 601 or permission of the instructor and programming experience equivalent to CS 303/350/355.

INFO 795                   Special Topics in Bioinformatics                                     3 hours
INFO 695

Topics of current research interest, such as metagenomics, microbiome, computational medicine, complex systems, deep learning in biology, artificial intelligence in biomedical, and translational bioinformatics applications. May be repeated as different sections taught by different instructors for credit. Permission of instructor is required.

INFO 798                   Bioinformatics Independent Study                              1-6 hours

Independent study to conduct bioinformatics research projects, guided by the instructor as the mentor. Permission of instructor and graduate program director is required.

VI. General Electives for Bioinformatics Majors

Computer Science Electives

CS 616                        Big Data Programming                                                    3 Hours

Introduction to Big Data, Properties of Big Data, platforms, programming models, applications, business analytics programming, big data processing with Python, R, and SAS, MapReduce programming with Hadoop.

CS 620                        Software Design and Integration                                    3 Hours

This course provides hands-on experience in the design and integration of software systems. Component-based technology, model-driven technology, service-oriented technology, and cloud technology are all explored. Software design basics, including the decomposition of systems into recognizable patterns, the role of patterns in designing software and design refactoring, and attributes of good design. Agile culture, CASE tools, tools for continuous integration, build, testing, and version control.
Prerequisites: CS 520 [Min Grade: B]

CS 621                        Advanced Web Application Development                     3 Hours

Introduction to web application design and development. Includes traditional web applications utilizing server-side scripting as well as client/server platforms. Covers responsive design for both mobile and desktop users, as well as hands on server provisioning and configuration. Other topics include web security problems and practices, authentication, database access, application deployment and Web API design, such as REpresentational State Transfer (REST).

CS 652                        Advanced Algorithms and Applications                        3 Hours

The design and analysis of fundamental algorithms that underpin many fields of importance ranging from data science, business intelligence, finance and cyber security to bioinformatics. Algorithms to be covered include dynamic programming, greedy technique, linear programming, network flow, sequence matching, search and alignment, randomized algorithms, page ranking, data compression, and quantum algorithms. Both time and space complexity of the algorithms are analyzed.

CS 663                        Data Mining                                                                      3 Hours

Techniques used in data mining (such as frequent sets and association rules, decision trees, Bayesian networks, classification, clustering), algorithms underlying these techniques, and applications.

CS 665                        Deep Learning                                                                  3 Hours

Deep Learning is a rapidly growing area of machine learning that has revolutionized speech recognition, image recognition and natural language processing. This course teaches you deep learning basics such as logistic regression, stochastic gradient descent, deep neural networks, convolutional neural networks and deep models for text and sequences. Students will also gain hands-on experience of using deep learning systems such as TensorFlow.

CS 667                        Machine Learning                                                            3 Hours

The course covers important issues in supervised learning, unsupervised learning and reinforcement learning. Topics include graphical models and Bayesian inference, hidden Markov model, mixture models and expectation maximization, density estimation, dimensionality reduction, logistic regression and neural network, support vector machines and kernel methods, and bagging and boosting.
Prerequisites: CS 660 (Artificial Intelligence)

CS 687                        Complex Networks                                                           3 Hours

Introduction to complex network theory and real-world applications in biology, physics, sociology, national security and cyber enabled technology systems such as social networks. Essential network models including small world networks, scale free networks, spatial and hierarchical networks together with methods to generate them with a computer will be discussed. In addition, various techniques for the analysis of networks including network modeling and evolution, community structure, dynamic network analysis, and network visualization will be explored.

Biostatistics Electives

BST 621                     Statistical Methods I                                                       3 Hours.

Mathematically rigorous coverage of applications of statistical techniques designed for Biostatistics majors and others with sufficient mathematical background. Statistical models and applications of probability; commonly used sampling distributions; parametric and nonparametric one and two sample tests and confidence intervals; analysis of two-way contingency table data; simple linear regression; simple analysis of variance designs with equal or proportional subclass members; use of contrasts and multiple comparisons procedures; introduction to survival analysis; multivariate methods. Interested students must have a year of calculus sequence before enrolling.

BST 675                     Introduction to Statistical Genetics                               3 Hours.

This class wil introduce students to population genetics, genetic epidemiology, microarray and proteomics analysis, Mendelian laws, inheritance, heritability, test cross linkage analysis, QTL analysis, human linkage and human association methods for discrete and quatitative traits.
Prerequisites: BST 611 [Min Grade: C] or BST 621 [Min Grade: C]

BST 676                     Genomic Data Analysis                                                   3 Hours.

Algorithms and methods that underlie the analysis of high dimensional biological data, as well as issues in the design and implementation of such studies. High dimensional biology includes microarrays, proteomics, genomic, protein structure, biochemical system theory and phylogenetic methods. NOTE: Some knowledge of statistics (MTH 180 or BST 621) also some bio-informatics/high dimensional biology training (CS 640, MIC 753, or BST 675 is required. Interested students are urged to contact the instructors with concerns regarding assumed knowledge.
Prerequisites: BST 611 [Min Grade: C] or BST 621 [Min Grade: C]

BST 680                     Statistical Computing with R                                         2 Hours.

This course is mainly focused on R and how to use R to conduct basic statistical computing. The course contains three themes: R programming, introduction to high performance computing, and basics of statistical computing.
Prerequisites: BST 621 [Min Grade: C] and BST 622 [Min Grade: C] and BST 626 [Min Grade: C] and BST 631 [Min Grade: C] and BST 632 [Min Grade: C]

Mathematics Electives

MA 515                      Probabilistic & Stat Reasoning                                      3 Hours.

Descriptive and inferential statistics, probability, estimation, hypothesis testing. Reasoning with probability and statistics is emphasized.

MA 516                      Numerical Reasoning                                                      3 Hours.

Develop understanding of number and improve numerical reasoning skills specifically with regard to place value, number relationship that build fluency with basis facts, and computational proficiency; developing a deep understanding of numerous diverse computational algorithms; mathematical models to represent fractions, decimals and percents, equivalencies and operations with fractions, decimals and percents; number theory including order of operations, counting as a big idea, properties of number, primes and composites, perfect, abundant and significant numbers, and figurate numbers; inductive and deductive reasoning with number.
Programming and mathematical problem solving using Matlab, Python, FORTRAN or C++. Emphasizes the systematic development of algorithms and numerical methods. Topics include computers, floating point arithmetic, iteration, functions, arrays, Matlab graphics, image processing, robotics, GNU/Linux operating system, solving linear systems and differential equation arising from practical situations, use of debuggers and other debugging techniques, and profiling; use of callable subroutine packages like LAPACK and differential equation routines; parallel programming. Assignments and projects are designed to give students a computational sense through complexity, dimension, inexact arithmetic, randomness, simulation and the role of approximation.
Prerequisites: MA 126 [Min Grade: C] or MA 226 [Min Grade: C]

MA 587                      Advanced Probability                                                     3 Hours.

Foundation of probability, conditional probabilities, and independence, Bayes theorem, discrete and continuous distributions, joint distributions, conditional and marginal distributions, convolution, moments and moment generation function, multivariable normal distribution and sums of normal random variables, Markov chains.
Prerequisites: MA 485 [Min Grade: B] or MA 585 [Min Grade: B]

MA 631. Linear Algebra. 3 Hours.

Vector spaces and their bases; linear transformations; eigenvalues and eigenvectors: Jordan canonical form; multilinear algebra and determinants; norms and inner products.

MA 650                      Differential Equations                                                     3 Hours.

Separable, linear, and exact first order equations; existence and uniqueness theorems; continuous dependence of solutions on data and initial conditions; first order systems and higher order equations; stability for two-dimensional linear systems; higher order linear systems; boundary value problems; stability theory.
Prerequisites: MA 642 [Min Grade: B]

MA 655                      Partial Differential Equations                                        3 Hours.

This course covers first order partial differential equations, elliptic equations, parabolic equations, and hyperbolic equations.
Prerequisites: MA 642 [Min Grade: C] or MA 650 [Min Grade: C]

Electrical and Computer Engineering Electives

EE 623                        Computer Vision                                                             3 Hours.

Advanced topics in computer vision: image segmentation, registration, and visual tracking. (EE 412:512 - Practical Computer Vision or EE 300 - Engineering Problem Solving + EGR 265 - Mathematical Tools for Engineering Problem Solving or other equivalent courses).

EE 634                        Introduction to Neural Networks                                  3 Hours.

Neural network topologies and learning algorithms with an emphasis on back propagation. Applications and limitations of networks. Designing networks for specific uses. Individual software project. A grade of C or better in EE 210 (Digital Logic) is required for this course.

EE 642                        Intelligent Systems                                                          3 Hours.

Organization and characteristics of intelligent systems. Optimization. Evolutionary algorithms. Neural network and fuzzy logic algorithms. Intelligent control.

EE 650                        Software Engineering                                                     3 Hours.

Introduces classical software lifecycles and software development paradigms. Provides state-of-the-art practical experience in proposal development and software design. Develops integrated skills drawing experience from computer engineering, computer science, communication, systems engineering, and problem solving.

Engineering Electives

BME 664                    Neural Computation                                                       3 Hours.

This course examines the principal theoretical underpinnings of computation in neural networks. Emphasis will be placed on understanding the relationship between the different approaches: dynamical systems, statistical mechanics, logic, Kalman filters, and likelihood/Bayesian estimation.

BME 665                    Computational Vision                                                     3 Hours.

This course approaches the study of biological and artificial vision from atheoretical perspective. We begin with a comparative survey of visual systems, and will examine vision algorithms and architectures.

BME 670                    Quantitative Physiology                                                  3 Hours.

Study of physiological problems using advanced mathematical techniques. Topics covered include: mechanics, fluid dynamics, transport, electrophysiology of cell membranes, and control systems.
Prerequisites: BME 517 [Min Grade: C] or ME 661 [Min Grade: C] or ME 567 [Min Grade: C] or ME 761 [Min Grade: C]

BME 680                    Biomolecular Modeling                                                   3 Hours.

We will teach molecular modeling principles and applications in this course. Throughout the course, students are offered hands-on exercises in molecular modeling tools and software. The course will help students understand the critical relationship among structure, function, and thermodynamic driving forces in structural biology, and be able to utilize molecular modeling techniques to explore biological phenomena at the molecular level.

Graduate Biomedical Sciences Electives

GBS 727                     Advanced Human Genomics                                        1-4 Hour.

This course will cover the conceptual basis, major discoveries, and unsolved problems in human genomics, with an emphasis on disease applications. The goal is to make students conversant with the structures, functions, and natural histories of human genomes, the computational and experimental methods used to establish that knowledge, the applications of genomics to medical research, and the broader impacts of genomic research on the community. Each topic will be covered by an approximately 90-minute lecture from a subject-specific PI coupled to reading of pieces of primary literature. Students will also participate in 3 student-led journal clubs in which one or more papers are discussed in detail with the help of the teaching faculty. We will also perform 3 interactive sessions to teach basic computational skills in Unix, Perl and R. Grading will be determined by: discussion interaction, computational problem sets due in weeks 4, 6, and 8, and a “final” project in which students perform a small but cohesive set of bioinformatic analyses to address a question of their choosing, subject to approval/discussion with the teaching faculty. Format: Each of the 7 weeks will include two, 90 minute lectures performed at UAB. In weeks 2, 4, and 6, we will convene at HudsonAlpha for four-hour sessions. Each four-hour session will include ~1 hour of paper discussion, ~1 hour of teaching on a relevant computational topic, and ~2 hours of hands-on interactive data manipulation with commonly used data types and computational tools. Course meets both on UAB Campus and at Hudson-Alpha in Huntsville.

GBS 757                     Biology of Disease                                                            3 Hours.

Biology of Disease is a comprehensive course in general pathophysiology designed for graduate students in the GBS program or other science related graduate programs. This course will begin with an overview of general anatomy and histology and then will investigate basic pathophysiologic principles emphasizing pathogenic mechanisms and clinically important diseases where current research areas will be highlighted. The biomedical science students will learn the mechanisms involved in disease processes and will develop an understanding of diseases and clinical medicine to help them converse knowledgeably with medical colleagues and target their research towards clinically relevant issues. Requirements: It is expected, although not required, that students will have a background in biochemistry, cell biology, microbiology, and immunology and will have successfully completed the first year GBS courses.

GBSC 724                  Metabolomics                                                                   3 Hours.

The goal of the course is to provide training on (1) the new vision of the chemical composition of the metabolome, (2) its impact on phenotypes in normal health and disease, (3) how to design experiments that (a)reduce systematic variation and (b) deal with the effects of the microbiome, (4) recovery of the metabolome from body fluids/excreta, cells and tissues, (5) analytical methods used in metabolomics, (6) post-acquisition data processing and univariate and multivariate statistical analysis, (7) metabolite confirmation, (8) unknown (new) metabolite identification, (9) pathway analysis, (10) targeted quantitative analysis of specific pathways, (11) use of stable-isotopically labeled precursors to measure pathway dynamics, (12) metabolomics in human and animal models of disease (atherosclerosis, cancer, diabetes, eye diseases, immune diseases and neurodegeneration), (13) metabolomics in situ (imaging mass spectrometry and direct analysis in the clinic and the operating room) and (14) integration of metabolomics with other 'Omics (genomics, transcriptomics and proteomics).

GBSC 728                  Cancer Genomics, Epigenetics, & Therapeutics          3 Hours.

Recent advances in high throughput technologies have enabled researchers to decipher the genomic and epigenetic alterations in cancer in great detail. In this course “Cancer Genomics and Epigenetics”, students will learn the technologies used for investigating the genomic and epigenetic alterations in cancer and effect of these changes on cancer progression and potential application of understanding these changes. The goal of this course is to provide the students with an exposure to a wide range of high throughput technologies used in cancer genomic research, basic and translational genomic and epigenetics research. In addition, the course will highlight the major discoveries in the area of gene mutations and gene fusions as well as therapeutic targeting some of the critical molecular alteration. This course will give exposures to students to state of the art cancer research topics, promotes scientific literacy, discussion skills, and critical research integration skills. In addition, students will also gain experience in presentation and ideas to develop new projects in cancer genomics and epigenetics research areas.

Biology Electives

BY 634                       Functional Genomics and Systems Biology                  3 Hours.

Systems biology is an inter-disciplinary study underlying complex biological processes as integrated systems of many interacting components. This course will give students a foundation in understanding complex biological interactions at the molecular, network and genomic level. This course will cover state-of-the-art high throughput established and novel approaches used in genome sequencing, transcriptomics, proteomics and metabolomics to obtain, integrate and analyze complex data. The students will also get familiar with knowledge on experimental perturbation of genomes, gene regulatory networks, comparative genomics and evolution, basic bioinformatics. This course will be a combination of text based lectures and discussions of the current literature relevant to Functional Genomics and Systems Biology. Prerequisite: BY210 minimum grade of C.

Health Professional Electives

HRP 650           Management and Leadership Skills for Clinical Professionals     1-3 Hour

Leadership concepts and management principles as employed by clinical professionals in health care organizations. Focus on effective approaches to communication, change and conflict management, performance and financial management, and cultural competence.