Spring-Summer 2022

Architectures and Applications of Deep Learning (Registration closed)

April 11 and 12 2022, 9 AM to 5 PM, NB2.100A

Explore the driving principles behind state of the art deep neural network architectures for generative modeling with GANS (CGAN, WGAN, InfoGAN, CycleGAN), unsupervised learning with autoencoders (CVAE), image analysis (Vision Transformers and CNNs), learning from limited data (Siamese nets), and sequence learning (LSTMs). Learning objectives for this practical course are: (1) Learn to implement these architectures using leading python frameworks: TensorFlow/Keras and PyTorch, (2) Learn design patterns that increase accuracy and network understanding, (3) Learn best practices to achieve winning performance. Throughout you will learn practical applications of deep learning for prediction from a wide array of domains including tabular data and high dimensional signal, image, and video data.

Applications are open to any person at UTSW or in the surrounding community who are interested in applying deep learning solutions to their machine learning research projects. For UTSW graduate students and trainees, academic credit (1 credit hour) is available. BME 5096-02 Special Topics - Architectures and Applications of Deep Learning, PDRT 5095-01 Special Topics in Bioinformatics - Architectures and Applications of Deep Learning

Class space is limited. Responses to this survey will be reviewed and acceptance in the course will be determined from the best match between participant experience and benefit derived from the training and the course content. Decisions will be conferred via email.

Lead Instructor: Albert Montillo

Other instructors: Michael Holcomb, Alex Treacher

Introduction to R for Beginners (Registration closed)

May 9th and 10th 2022, 9 AM to 5 PM; NG3.202

Get introduced to using R as a data analysis language. During this course, the student will be able to install and use R for basic data analyses projects. We will demonstrate how to read, write, and format data for appropriate analysis, testing, and tabulation. This course will be a hands-on experience with problems and a variety of result reproduction exercises in R. Advanced topics and extensions will be taught in a subsequent nanocourse.

Applications are open to any person at UTSW or in the surrounding community who are interested in learning R for their data analysis in research. Class space is limited. Responses to this survey will be reviewed and acceptance in the course will be determined from the best match between participant experience and benefit derived from the training and the course content. Decisions will be conferred via email. For UTSW graduate students and trainees, academic credit (1 credit hour) is available. BME 5096-03 Special Topics - Introduction to R for Beginners, PDRT 5139-01 BIOINFORMATICS INTRODUCTION TO R FOR BEGINNERS LEVEL I

Registration closed, nanocourse full.

Lead Instructor: Christopher Chaney

Other instructors: Amit Amritkar, Micah Thornton

Introduction to NGS Analysis (Registration closed)

May 19th and 20th 2022, 9 AM to 5 PM; NB2.100A

This course will cover the basics of next-generation sequencing (NGS) technologies and computational analysis. We will provide an overview of NGS sequencing of DNA, RNA, and ChIP, and explain the FASTQ file format. This course includes hands-on practice for class participants to perform sequence alignment using programs such as BWA, Bowtie, and HISAT, and examine alignment output. We will also explore various aspects such as quality control of sequencing data and key algorithms behind sequence alignment and variant calling programs.

Applications are open to any person at UTSW or in the surrounding community who are interested in using NGS analysis for their sequencing datasets. Class space is limited. Responses to this survey will be reviewed and acceptance in the course will be determined from the best match between participant experience and benefit derived from the training and the course content. Decisions will be conferred via email. For UTSW graduate students and trainees, academic credit (1 credit hour) is available. BME 5096-04 Special Topics - Introduction to NGS Analysis, PDRT 5095-02 Special Topics in Bioinformatics - Introduction to NGS Analysis

Registration closed, nanocourse full.

Lead Instructor: Bo Li

Other instructors: Daehwan Kim, Christopher Chaney, Micah Thornton

Advanced Concepts of Deep Learning (Registration closed)

June 13th and June 16th 2022, 9 AM to 5 PM; NB2.100A

This course will provide an introduction to key and emerging concepts and ideas in deep learning. The 1st part of this course will introduce the design and principle behind recent advances in model architecture: transformers (including several efficient transformer designs), graph neural networks, and several other new architectures that utilize attention-like multiplicative updates. The 2nd part of this course will cover mathematics and algorithms of generative probabilistic modeling with deep learning, including energy-based models, variational autoencoder, generative adversarial network, normalizing flow, neural ODE, and diffusion probabilistic models. Conceptual advances will be the focus of this nanocourse. You are encouraged to bring a modeling problem of interest for the group discussion session.

Applications are open to any person at UTSW or in the surrounding community who are interested in learning about advancements in deep learning. Class space is limited. Responses to this survey will be reviewed and acceptance in the course will be determined from the best match between participant experience and benefit derived from the training and the course content. Decisions will be conferred via email. For UTSW graduate students and trainees, academic credit (1 credit hour) is available. BME 5096-05 Special Topics - Advanced Concepts of Deep Learning, PDRT 5095-03 Special Topics in Bioinformatics - Advanced Concepts of Deep Learning (Note: this course is advanced and requires some basic knowledge of machine learning, deep learning, and programming)

Registration closed, Nanocourse full

Instructor: Jian Zhou

Advanced NGS Analysis (Registration closed)

June 27th and 28th 2022, 9 AM to 5 PM; NB2.100A

This course covers a few advanced yet useful topics regarding next-generation sequencing (NGS) computational analysis. The course includes hands-on practice for class participants to perform HLA gene typing and haplotype-resolved assembly, gene expression quantification using Kallisto and Salmon, and differential gene expression analysis using DESeq2. (Particular subtopics are subject to change as we refine the course.) Along with this practice, the course will explore core algorithms and statistical models utilized in the programs featured in the course.

Applications are open to any person at UTSW or in the surrounding community who are interested in learning about advanced NGS analysis. Class space is limited. Responses to this survey will be reviewed and acceptance in the course will be determined from the best match between participant experience and benefit derived from the training and the course content. Decisions will be conferred via email. For UTSW graduate students and trainees, academic credit (1 credit hour) is available. BME 5096-06 Special Topics - Advanced NGS Analysis, PDRT 5095-04 Special Topics in Bioinformatics - Advanced NGS Analysis (Note: this course is advanced and requires prior knowledge of NGS analysis methods and applications in general).

Registration closed. Nanocourse full.

Lead Instructor: Daehwan Kim

Other instructors: Bo Li, Micah Thornton

Single Cell Genomics (Registration closed)

July 18th and 19th 2022, 9 AM to 5 PM; location NB2.100A

This course covers the basics of single-cell technologies and computational analysis. We will provide overviews and key algorithms for single-cell RNA-Seq, single-cell ATAC-Seq, and multiome analysis. This course includes hands-on practice to perform analyses from raw data to quality control, clustering, visualization, and trajectory inference. It also includes more advanced topics including multiome analysis, spatial transcriptomics, and single-cell perturbation. This course requires proficiency with R and Python.

Applications are open to any person at UTSW or in the surrounding community who are interested in learning about advanced NGS analysis. Class space is limited. Responses to this survey will be reviewed and acceptance in the course will be determined from the best match between participant experience and benefit derived from the training and the course content. Decisions will be conferred via email. For UTSW graduate students and trainees, academic credit (1 credit hour) is available. BME 5096-07 Special Topics - Single Cell Genomics, PDRT 5095-05 Special Topics in Bioinformatics - Single Cell Genomics

Registration closed, nanocourse full.

Lead Instructor: Genevieve Konopka

Other instructors: Gary Hon, Tao Wang, Ashwinikumar Kulkarni, Emre Caglayan, Yi Han, Yihan Wang, Daniel Armendariz