Bioinformatics MS
Degree Awarded: Master of Science (MS)
Minimum Required Credits: 30.0
Co-op Option: Available for full-time, on-campus master's-level students
Classification of Instructional Programs (CIP) code: 26.1103
Standard Occupational Classification (SOC) code: 15-1221; 15-1299; 15-2051; 19-4021; 15-1252
About the Program
The Bioinformatics program aims to train professional graduates for bioinformatics specialist roles in healthcare, biomedical research, pharmaceutical, and biotechnology industries by providing them with interdisciplinary knowledge and experience to develop and apply sophisticated computational methods for the analysis of biomedical data. The unique interdisciplinary program consists of courses and elective concentrations offered by the School of Biomedical Engineering and Science (Bioinformatics, Neuroengineering), the School of Computing and Information Sciences (Data Science), the School of Engineering (Machine Learning) and the Graduate School of Biomedical Science and Professional Studies (Drug Discovery and Development).
The MS program in Bioinformatics can be taken full- or part-time with optional online course offerings. The program provides advanced, hands-on coursework in core and applied areas of bioinformatics and systems biology.
Master's students can choose to include a six-month graduate co-op cycle as part of their studies, supported by Drexel's Steinbright Career Development Center.
Graduates in bioinformatics can enter diverse, high-growth sectors across industry, healthcare and academia. Graduating students work in the pharmaceutical industry in areas such as drug discovery and development. In healthcare settings, clinical bioinformaticians work alongside medical professionals to analyze patient sequencing data, directly informing personalized oncology treatments and rare disease diagnoses. For those drawn to research, academic and government institutions offer pathways as data scientists or core facility managers, supporting large-scale public health initiatives or ecological modeling projects. Alternatively, many graduates pursue advanced degrees (Ph.D.) to pioneer new algorithmic tools, machine learning architectures, and statistical frameworks for complex biological data.
Additional Information
For more information about this program, please contact the School of Biomedical Engineering and Science
Admission Requirements
Acceptance into the MS in Bioinformatics program requires a four-year bachelor's degree in sciences or engineering from a regionally accredited institution in the United States or an equivalent international institution. Regular acceptance typically requires a minimum cumulative grade point average of 3.0.
Degree Requirements
| Core/Foundational Courses | ||
| Select one (1) course from the following: | 3.0 | |
| Advanced Design and Analysis for Biomedical Studies | ||
| Biostatistics II | ||
| Optional Statistics Elective: | 0.0-3.0 | |
| Interpretation of Data | ||
| Select one (1) course from the following: | 3.0 | |
| Biomedical Computing: Introduction to Programming in Matlab and Python | ||
| Software Design and Development for Biomedical Problems | ||
| BMES 5652 | Bioinformatics: Transcriptomics and Functional Genomics | 3.0 |
| BMES 5662 | Algorithms in Bioinformatics and Genomic Analysis | 3.0 |
| Optional AI Elective: | 0.0-3.0 | |
| ML-AI Foundations in Biomedical Applications | ||
| Subject Areas Focused Courses | ||
| Select one concentration from the options below: | 18.0 | |
| Optional Co-op Experience | ||
| Co-op is an option for this degree for full-time on-campus students. To prepare for the 6-month co-op experience, students are required to complete COOP 5000. | ||
| Total Credits | 30.0-36.0 | |
Concentrations
Advanced Therapeutics
| Select three (3) of the following: | 9.0 | |
| Advanced Biomaterials | ||
| Immune Engineering | ||
| Tissue Engineering | ||
| Design and Manufacturing of Advanced Therapeutics | ||
| Total Credits | 9.0 | |
Data Science
| IS 5310 | Introduction to Data Science | 3.0 |
| IS 5320 | Principles of Data Analysis | 3.0 |
| Select one (1) elective course: | 3.0 | |
| Programming Data Structures and Algorithms | ||
| Principles of Optimization and Inference | ||
| Applied Machine Learning | ||
| Information Behavior | ||
| Total Credits | 9.0 | |
Drug Discovery and Development
| PHRM 525S | Drug Discovery and Development I | 3.0 |
| PHRM 526S | Drug Discovery and Development II | 3.0 |
| Select one (1) elective course: | 3.0 | |
| Business Processes and Contemporary Concerns in Pharmaceutical R & D | ||
| Introduction to Clinical Pharmacology | ||
| Informatics in Pharm Res & Development | ||
| Regulatory, Scientific and Social Issues Affecting Biotech Research | ||
| Graduate Pharmacology | ||
| Total Credits | 9.0 | |
Machine Learning
| ECE 5700 | Probability and Random Variables | 3.0 |
| ECE 5704 | Applied Machine Learning Engineering | 3.0 |
| ECE 5740 | Bioinformatics | 3.0 |
| Optional Electives: | ||
| Pattern Recognition | ||
| Deterministic Signal Processing | ||
| Statistical Analysis of Genomics | ||
| Cell and Tissue Image Analysis | ||
| Total Credits | 9.0 | |
Neuroengineering
| BMES 5711 | Neuroengineering Cells and Signals | 3.0 |
| BMES 5722 | Neuroimaging and Brain Computer Interfaces | 3.0 |
| BMES 5733 | Computational Neuroscience and Neuroengineering | 3.0 |
| Optional Elective: | ||
| Frontiers in Neuroscience | ||
| Total Credits | 9.0 | |