Credits: 3

Category: Advanced Bioinformatics and Computational Biology 

An introduction to the use of continuous and discrete differential equations in the biological sciences. Modeling in biology, physiology and medicine. 

Credits: 1

Category: Seminars/Discussion 

Restricted to incoming Bioinformatics students only.  

This course covers topics to help incoming Bioinformatics graduate students succeed and immerses students into the department. Topics include finding a mentor, a bioinformatics research area, and career path; using library, computational, and funding resources; writing papers and student grants. 

Credits: 3

Category: Elective
Pre-req: Calc II or equivalent 

The course provides a review of some of the fundamental mathematical techniques commonly used in bioinformatics and biomedical research. These include: 1) principles of multi-variable calculus, and complex numbers/functions, 2) foundations of linear algebra, such as linear spaces, eigen values and vectors, singular value decomposition, spectral graph theory and Markov chains, 3) differential equations and their usage in biomedical system, which includes topic such as existence and uniqueness of solutions, two dimensional linear systems, bifurcations in one and two dimensional systems and cellular dynamics, and 4) optimization methods, such as free and constrained optimization, Lagrange multipliers, data denoising using optimization and heuristic methods. MATLAB, R and Python will be introduced as tools to simulate/implement the mathematical ideas. 

Credits: 1

Category: Basic Skills and Rigor
Offered Fall term as 1 week workshop in late August.
Pre-req: programming skills and 1 year of graduate courses

This course fulfills the new NIH requirements for rigor & reproducibility. It covers how to carry out rigorous, transparent, and reproducible computational biomedical research. Specific topics include developing a rigorous study design, data quality control and processing, rigor & transparency for code and software, following the FAIR principles, and dissemination of data and software.

Credits: 3

Category: Advanced Bioinformatics 

This course provides an introduction to mathematical and computational modeling for both experimentally and theoretically inclined students, as well the currently employed strategies to investigate physiological problems with computational modeling. In our course, we select important physiological problems whose solution will involve some useful computational modeling. After briefly discussing the required scientific background, we formulate a relevant computational problem with some care. The formulation step is often difficult. Not many courses or textbooks actually demonstrate this. In our course, we plan to give due emphasis to the challenges involved in constructing computational models. The goals of this approach is to empower students to build their own models and become effective performers of systems and computational physiology research.

Credits: 3

Category: Molecular Biology

Introduces basic biology to graduate students without any prior college biology. Geared towards students in Bioinformatics, Biostatistics, or other computational fields who have quantitative training (computer science, engineering, mathematics, statistics, etc.). Will cover major topics related to biomedical research including: organic and biochemistry, molecular biology, genetics, cell biology, and microbiology.

Credits: 3

Category: Related Bioinformatics Courses

This course provides an introduction to the principles and practical approaches of bioinformatics as applied to genes and proteins. The overall course content is broken down into sections focusing on foundational information, statistics, and systems biology, respectively.

This course replaces BIOINF 525. 

Credits:4

Category: Non-major courses in Bioinformatics/Introductory Bioinformatics 

Students will be introduced to the fundamental theories and practices of Bioinformatics through a series of integrated lectures and labs. A broad range of topics will be covered illustrating how bioinformatics is shaping the modern landscape of biomedical research. Students develop practical skills for processing, visualizing, and analyzing high-throughput biomedical data.

If any questions, please contact the Course Director, Prof. Stephen Guest ([email protected]).

Credits: 3

Category: Introductory Bioinformatics 

This course introduces Bioinformatics Program students to common topics in bioinformatics as well as corresponding computational approaches in those areas. Students will learn how to implement and apply various algorithms and statistical models to solve challenging problems and will also build a foundation for developing tools for future technologies. 

Credits: 4

Category: Probability and  Statistics

An introductory yet a rigorous course in probability and statistics for graduate students in the Bioinformatics program. This course is intended for students with a solid background in college-level calculus to build a strong foundation in probability and statistics. The first half of the course will focus on probability and its applications to information theory and coding theory: axioms of probability, conditional probability, independence, Bayes’ formula, discrete random variables, binomial, geometric, negative binomial Poisson distributions, densities, expected values, variance, covariance, continuous random variables, exponential and normal distributions, joint distributions, law of large numbers, central limit theorem, Shannon entropy, Jensen’s inequality, Kullback-Leibler divergence, mutual information. Dimensionality techniques such as PCA, t-SNE, UMAP will also be covered. The second half of the course covers statistical inference and modeling: univariate numeric data testing, univariate categorical data testing, bivariate numeric data testing, bivariate categorical data testing, simple and multiple linear regression, ANOVA, Polynomial regression, model expansion, model selection, shrinkage methods, and multiple hypothesis testing.

Credits: 3

Category: Advanced Bioinformatics and Computational Biology

This course explores methods and principles for constructing structure and function of biological networks using real datasets. After introducing basic linear algebra and MATLAB, we will discuss properties of networks, genomics technologies, spectral graph theory, Laplacians (Fiedler number and Fiedler vector), Network inference and controllability, Dynamic Mode Decomposition, Tensor Factorizations. 

Credits: 3
Category: Advanced Bioinformatics and Computational Biology

Prerequisites: Graduate Standing and STATS 400, BIOSTAT 523, BIOSTAT 553 or equivalent (or permission of instructor)

This course will cover statistical methods used to analyze data in experimental molecular biology, with an emphasis on gene and protein expression array data. Topics: data acquisition, databases, low level processing, normalization, quality control, statistical inference (group comparisons, cyclicity, survival), multiple comparisons, statistical learning algorithms, clustering visualization, and case studies.

Credits: 3

Category: Advanced Bioinformatics and Computational Biology 

This course is open to graduate students and upper-level undergraduates in applied mathematics, bioinformatics, statistics, and engineering, who are interested in learning from data. Students with other backgrounds such as life sciences are also welcome, provided they have maturity in mathematics. I will start with a very basic introduction to data representation as vectors, matrices (graphs, networks), and tensors. Then I will teach geometric methods for dimension reduction (manifold learning, diffusion maps, t-distributed stochastic neighbor embedding (t-SNE), etc.) and topological data reduction (introduction to computational homology groups, etc.). I will bring an application-based approach to spectral graph theory, address the combinatorial meaning of eigenvalues and eigenvectors of matrices associated with graphs, and discuss extensions to tensors [1, 2]. I will also provide an introduction to the application of dynamical systems theory to data [3, 4]. Real data examples will be given wherever possible and I will work with you to solve these examples. The methods discussed in this class are shown primarily for biological data, but are useful in handling data across many fields. 

Open to upper-level undergraduates and graduate students

Credits: 3

Category: Advanced Bioinformatics and Computational Biology 

Introduction to proteomics and metabolomics, mass spectrometry, peptide identification and protein inference, statistical methods and computational algorithms, post-translational modifications, genome annotation and alternative splicing, quantitative proteomics and differential protein expression analysis, protein-protein interaction networks and protein complexes, targeted and untargeted metabolomics and lipidomics, data mining and analysis of large-scale data sets, clinical applications, data integration and systems biology. 

Credits: 3

Category: Advanced Bioinformatics and Computational Biology 

This course focuses on discovering the way in which spatial variation influences the motion, dispersion, and persistence of species. Specific topics may include i) Models of Cell Motion: Diffusion, Convection, and Chemotaxis; ii) Transport Processes in Biology; iii) Biological Pattern Formation; and iv) Delay-differential Equations and Age-structured Models of Infectious Diseases. 

Credits: 3

Category: Advanced Bioinformatics and Computational Biology 

Computational neuroscience provides a set of quantitative approaches to investigate the biophysical mechanisms and computational principles underlying the function of the nervous system. This course introduces students to mathematical modeling and quantitative techniques used to investigate neural systems at many different scales, from single neuron activity to the dynamics of large neuronal networks. 

Credits: 4

Category: Computing and Informatics 

Programming Laboratory in Bioinformatics --- This course introduces the principles of general computer programming and relational databases as tools to solve problems in bioinformatics data analysis. General programming and graph generation is taught using the object oriented language Python but some variations may occur. The relational database language SQL is taught in conjunction with database design, construction and querying. Packages that extend the capabilities of Python are explored. Grades are based on homework, quizzes, participation in class discussions, and cooperative development of a group project or homework.

Some familiarity with programming concepts is recommended, but motivated students with knowledge of a bioinformatics application area and a logical approach to problem solving can succeed in this course.

Credits: 3

Category: Computing and Informatics, Advanced Bioinformatics and Computing

This class presents and implements all the steps and phases of software development (design, implementation, documentation, issue tracking, peer review, and release). Students can choose an already implemented tool and add a new feature to it or implement a new tool. Students should be familiar with R or Python programming languages. Instructor permission is required. 

Credits: 3

Category: Advanced Bioinformatics and Computing and Informatics

The course covers signal processing, image processing, artificial intelligence (AI) and machine learning (ML) methods with an emphasis on their applications in medicine and biology. Students will need a basic understanding of linear algebra for this course. 

Topics include: 1) Transforms and feature extraction – Fourier transform, wavelet transformation, fundamentals of information in theory. 2) Introduction to AI and ML – predictive vs generative AI, clustering vs classification, Naïve Bayes, Classification and Regression Trees. Random Forest, Support Vector Machines, introduction to Neural Networks. 3) introduction to conventional image processing methods, 4) Introduction to deep learning methods, 5) Introduction to generative AI, 6) Applications in medicine and biology.

Credits: 3

Category: Advanced Bioinformatics and Computing and Informatics 

BIOINF 585 is a project-based course focused on deep learning and advanced machine learning in bioinformatics. The course will be comprised of deep learning and some other traditional machine learning in applications including regulatory genomics, health records, and biomedical images, and computation labs. The course is project-based, including two in-class projects and one at-home project, aimed at generating publication-quality reports. 

Credits: 3

Category: Advanced Bioinformatics and Computing and Informatics 

This course intends to build on the fundamentals of signal processing and machine learning to explore concepts from these areas in the context of cancer bioinformatics. Motivating examples from cancer genomics, cancer imaging and drug discovery will be used to examine these principles. The course will comprise instructor-led lectures, student lectures, and course projects.

Pre-requisite: BIOINF 580 or instructor consent.

Credits: 3

Category: Advanced Bioinformatics and Computational Biology

This course introduces the foundational machine learning techniques used in computational biology and describes their applications to biological data. The course emphasizes theoretical foundations and practical implementation of the techniques, in addition to the biological background needed for computational biology applications. 

Expertise in programming, calculus, linear algebra, probability, and familiarity with multivariate calculus is required.

Credits: 3

Category: Advanced Bioinformatics & Computational Biology

This course introduces fundamental concepts and methods of chemoinformatics, structural biology, and machine learning. Discussion includes breakthroughs in scientific foundation model application, plus generalizability of drug discovery predictions. Emphasis is on understanding fundamental concepts in bioinformatics and practical application of data science tools and methods to problems in medicinal chemistry.

Course directors:  Matt O’Meara, Terra Sztain

Credits: 3

Category: Advanced Bioinformatics & Computational Biology

BIOINF 597 covers both some of the conventional AI and some of the recently developed deep learning and generative AI methods, beyond those covered in BIOINF 580. We will discuss classic AI methods such as agent-based models and probabilistic methods (such as Hidden Markov Models and Bayesian network), interpretable methods (such as fuzzy models and fuzzy neural networks). We will also discuss more recently developed methods such reinforcement learning, active learning and representation learning. We will emphasize some emerging topics in deep learning such as attention mechanisms, transformers and generative models.

Credits: 1

Category: Seminars/Discussions 

Bioinformatics Journal Club entails a weekly discussion of current and classic papers concerning biology on a whole-genome scale or using genome sequence-based approaches. It is a great opportunity for students and researchers to be exposed to current topics of Bioinformatics. Although the presentations are on a volunteer basis, participants are encouraged to present. Each week's paper is chosen by the presenter a week in advance. Journal Club is open to anyone interested in participating. This course is for first-year students who have not taken a journal club before. No presentation is required. 

Credits: 1

Category: Seminars/Discussions 

Many areas of biology and medicine now involve massive quantities of data or physical analysis; analysis of such data is considered fundamental for the advancement of biomedical science. In this journal club readings and discussion of current research literature acquaint students with biological quantitative methods and research questions being applied to new data. Students presents and discuss papers, plus critique publication and presentation. Registered students must present background and discuss in detail articles focused on emerging topics and questions related to bioinformatics. 

Credits: 3

Category: Advanced Bioinformatics and Computational Biology

Advanced course in population genetics, focusing on mathematical models and statistical models for data analysis. Topics include infinite and finite population phenomena, population structure, admixture, mutation models, coalescent methods, recombination, and linkage disequilibrium.