Dr. Mary Yang

Fall 2013 - Spring 2020

BINF 4445/5445 Bioinformatics Theory and Applications
This course introduces theoryand application of bioinformatics. The topics include algorithms, sequencealignment, biological databases, phylogeny, gene finding, regulatory elementsprediction, genome annotation, integrative genomics, comparative genomics, proteinstructure prediction and visualization, next-generation sequencing dataanalysis and systems biology. For graduate students, this class is a corecourse of the UALR-UAMS Joint Graduate Program in Bioinformatics. For undergraduatestudents, this course is the final course for bioinformatics minor.

INFC 4339/5339 Introduction to R programming
This course is designed toprovide a comprehensive introduction to R. The R language offers a rich environment forworking with data. Students will learn how to use the programming language andstatistical environment R to perform scientific computing and datavisualization. This is a hand-on, project-based course to enable students todevelop skills for solving data science problems using R.

BINF 3345 Introduction to Bioinformatics
This course is designed tointroduce students from a variety of life science or computing disciplines tobioinformatics concepts, principles, tools, and analysis methods. Uponcompletion of the course, students should be able to apply bioinformaticstechniques to solve problems in their own research, work with large-scalebiological and medical data sets, and use online bioinformatics tools.

BINF 7193 Bioinformatics Graduate Seminar

R Summer Course (June 14, 2023)

Visualization in R with ggplot, Plotly and Leaflet R markdown file | ds_salaries.csv | Table2.png | country-capitals.csv

R Summer Course (June 16, 2022)

ggplot in R and Python Slides Download | R markdown file | ggplot.ipynb | Cancer-rates-DATA.csv

R Summer Course (June 17, 2021)

Working with Data in R Slides Download | R Markdown code Download | html file produced by R Markdown | R exercise solution.Rmd | Data file

R/Bioconductor Workshop (August 2-4, 2017)

Topic one : Introduction to R. Slides Download | Exercise Download | R script Download

Topic two : Data Visualization. Slides Download | R script Download

Topic three : Machine Learning. Slides Download | R script Download | Test Data Download | Train Data Download