Bioinformatics and Systems Medicine focuses on molecular, cellular, and organ levels of the human biological system. Students and researchers with a focus on this domain develop and apply computational methods and tools for the study of genes, proteins, cells, biological networks, and images and integrate them with clinical and population data to address fundamental challenges in patient care. Topics include Precision Medicine, Genomics, Pharmacogenetics, Genetic Sequencing and Analysis (Single Cell, Exome, Whole Genome, etc.), Proteomics, Imaging Informatics, Biological Network, Smart Clinical Trials, Multimodality Modeling, and the integration of genotyping and phenotyping for patient care. Students and researchers in Bioinformatics and Systems Medicine work closely with those in Health Data Science and AI and Clinical and Health Informatics.
Students can pursue an education in Bioinformatics and Systems Medicine under the following academic programs: Graduate Certificate, Master of Science (MS), Doctor of Philosophy (PhD). Students are admitted to the school, not a specific department, so that they can obtain a broad and comprehensive education experience while specializing with an in-depth training in the three areas offered by the Department of Health Data Science and Artificial Intelligence, Department of Clinical and Health Informatics, and Department of Bioinformatics and Systems Medicine.
In addition to a core set of foundational courses for all concentrations, the following are selected courses focusing on Bioinformatics and Systems Medicine.
Course Description:
The course gives a comprehensive entry-level introduction to bioinformatics. It covers a wide variety of topics in bioinformatics, including but not limited to genome analysis, transcription profiling, protein structure and proteomics. Two major goals are 1) to help students understand the scope, basic concepts and theory of bioinformatics; and 2) to become familiar with tools for bioinformatics-related data analysis. Using software tools will be a major component of the course but advanced programming skills are not required. A laptop computer is necessary to use the bioinformatics software and tools in class and while performing the research tasks for the course project.
Course Description:
Pharmacogenomics is the study of how human genetic variation impacts drug response. It is one of the major promises of the genome project: that individual genetic information can be used to tailor drugs to patients, maximizing efficacy and minimizing adverse reactions. An understanding of pharmacogenomics requires dual understanding of the basics of genetics and genomics and of pharmacology. This course will provide the background to understand the current state and literature in pharmacogenomics, including the methods used in research and the current issues in discovery and implementation of pharmacogenomics.
Course Description:
This course provides students practical skills and statistical concepts and methods that underlie the analysis of high-dimensional genomic and Omics big data generated by high throughput technologies. It will also address issues related to the experimental design and implementation of these technologies. Lectures will often be delivered with live demonstrations. Students will engage in practical problem solving sessions. The R language will be used for programming throughout the course.
Course Description:
Systems medicine is an interdisciplinary field of study that looks at the systems of the human body as part of an integrated whole, incorporating biochemical, physiological, and environment interactions. Systems medicine draws on systems science, omics, imaging, systems biology, and considers complex interactions within the human body in light of a patient's genomics, behavior and environment, and design the precision medicine at systems level. Students will engage in hands-on projects exploring methods of systems medicine.
Course Description:
Biomedical data in the form of images, videos or other unstructured signals are continuously collected by clinicians, such as radiologists, dermatologists or ophthalmologists, life science researchers and increasingly by ourselves with our personal devices. Tools able to distill quantitative actionable information from these data are essential to generate phenotypes, aid diagnosis, screening, treatment and automate repetitive tasks. In the era of personalized medicine and big data, they have become an urgent medical need. In this course, you will be introduced to the essential pattern recognitions techniques to build and evaluate such tools. We will be covering the basics of image/signal processing, computer vision and applied machine learning with hands on examples relevant to biomedical applications.
Course Description:
This course will provide the foundations of precision medicine and its relations with genomics by exposing trainees to the use and interpretation of genetic studies of human populations in the context of phenotypes and diseases. The course will cover principles of genetics underlying associations between genetic variants and disease susceptibility and drug response.
Course Description:
Bioinformatics play significant roles in modern genetics and genomics studies. Nearly every large-scale biology projects require a significant component of bioinformatics and computational approaches. This course provides an introduction to advanced technologies and resources in genetics, epigenetics, transcriptomics, and phenotype studies, organized as “topics”. Students will be provided with knowledge and skills to apply canonical algorithms in bioinformatics tasks, to identify potential challenges, and to develop their own analysis pipelines.
Course Description:
Seminar in Precision Medicine will introduce and discuss recent advances, frontier technologies, case studies, and future direction in precision medicine. The topics cover precision medicine, bioinformatics, systems biology, pharmacogenomics, genetics, genomic medicine, study design, methodologies and computational tools. Students enrolled in the course for credit are required to give a seminar presentation, attend at least 80% of the weekly seminars, and fill out evaluation forms. Each student seminar must be supervised by a faculty member (not necessarily the student's advisor). The faculty member will work with students to ensure that the seminars are both appropriate and interesting for the audience.
Course Description:
This course introduces a few common deep learning architectures (e.g., convolution neural network, graph neural network, recurrent neural network and autoencoder) to the students who are new to AI. The primary aim of this course is to flatten the learning curve in AI and to provide students with a basis for further implementation of more complex models using enormous real-world data, especially in cancer research.
This course will have a combination of lectures and demos to guarantee the students will have adequate first-hand experience with course concepts and with the opportunity to explore AI methods implemented in cancer research. We also include one tutorial of basic programming skills with Python and its machine learning libraries.
We crunched the numbers and they don't lie.
| Career Outcomes for Bioinformatics and Systems Medicine | ||
| Average Salary | Average Salary Range | |
| Houston | $157,160 | $77,000 - $286,000 |
| Texas | $110,088 | $58,000 - $257,000 |
| Nationwide | $143,698 | $86,000 - $298,057 |
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