Events by this organizer

This is an introductory two-part course that focuses on data and project management using R and RStudio. RStudio makes it possible to work on a complete research project in a more efficient, integrated, and organized manner. RStudio also connects with Git and Github, and learners will have a chance to experiment with this integration and understand its advantages for collaboration and version control. Some familiarity or experience in R and RStudio is recommended but not required. Participants are encouraged to install R, RStudio and create a GitHub account before the course so that they can follow along with the instructor. Participants must register separately for Part 2 of this class.

This is an introductory two-part course that focuses on data and project management using R and RStudio. RStudio makes it possible to work on a complete research project in a more efficient, integrated, and organized manner. Participants will learn how to create reproducible documents that combine code, analysis, and narrative. This intermediate-level course is designed to be relevant to students from different disciplines. Some familiarity or experience in R and RStudio is recommended but not required. Participants are encouraged to install R, RStudio and create a GitHub account before the course so that they can follow along with the instructor. Participants must register separately for Part 1 of this class.

In partnership with the NIH Clinical Center’s Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering a class to assist researchers in writing a manuscript. Participants will become familiar with statistical issues that can arise as well as recommendations to prevent them. At the end of the class, participants should have a good sense of what to do and not to do when writing the statistical sections of a manuscript. Most of the examples will be related to clinical research; however, anyone can benefit from the tips shared. Plenty of time will be devoted for questions, and references will be provided for more in-depth self-study.

This class complements, How to Write a Research Paper Parts 1 & 2, as it specifically covers key points in writing the statistical portion of a manuscript – particularly for clinical research. Attendance to How to Write a Research Paper is not required to benefit from this class.

What’s the difference between “regular” statistics (i.e. what you may have been using in the past several years) and the “new” Bayesian statistics? Which one should you use for your next study?

This is Part 1 of a two-part lecture series intended for non-statisticians interested in understanding the basic, intuitive thinking behind the two schools of statistical inference: frequentist (known as classical) and Bayesian. Part 1 will address the frequentist approach, and will cover the concepts of hypothesis testing, confidence intervals, Type I and Type II errors, statistical power, and p-values. Technical details will be kept to an absolute minimum.

Although you may attend only one part of this series, attending both parts will give you a better sense of the contrast between these two statistical approaches. During the class, time will be devoted to questions from attendees and references will be provided for in-depth self-study. You must register separately for Part 2 of this class series.

What’s the difference between “regular” statistics (i.e. what you may have been using in the past several years) and the “new” Bayesian statistics? Which one should you use for your next study?

This is Part 2 of a two-part lecture series intended for non-statisticians interested in understanding the basic, intuitive thinking behind the two schools of statistical inference: frequentist (known as classical) and Bayesian. Part 2 will address the Bayesian approach, and will cover the concepts of Bayes’ Theorem, prior and posterior distributions, and Bayes factor. Technical details will be kept to an absolute minimum.

Although you may attend only one part of this series, attending both parts will give you a better sense of the contrast between these two statistical approaches. During the class, time will be devoted to questions from attendees and references will be provided for in-depth self-study. You must register separately for Part 1 of this class series.

Learn how to get started on using QIAGEN’s Ingenuity Pathway Analysis (IPA) to quickly identify biological relationships, mechanisms, pathways, functions and diseases most relevant to experimental datasets. IPA is cited in tens of thousands of peer-reviewed articles. The class will demonstrate how to explore IPA’s knowledge & discovery tools to relate the most recent literature findings to your research. The demo session will mainly focus on biological interpretation of expression data but will also cover multi-omics analysis including variant data and phosphoproteomics.

Getting Started: fundamentals of IPA; overview of key features; search & pathway building; advanced search; building & editing pathways; using Build & Overlay tools. Dataset Analysis: data upload & analysis; interpretation of gene, transcript, protein & metabolite data; pathway analysis & canonical pathways; downstream effects & interpreting the heat map; causal regulators and their directional effect on genes, functions and diseases across multiple time points or doses; interpreting networks; comparison & multiple observations analysis; miRNA and isoform analysis; BioProfiler: define relationships between molecule activity and diseases/processes in the literature. Q & A session: A QIAGEN Ingenuity Field Applications Scientist will be available to answer individual questions or discuss specific projects.

This is the first course in a four-part series on data visualization in R. A basic understanding of R is expected. This class provides a basic overview using ggplot, which is a part of the tidyverse. The tidyverse is a collection of R packages designed for data science. Participants are encouraged to install R and RStudio before the course so that they can follow along with the instructor. Participants will need to download the class data before the webinar. Separate registration is required for each course in the series.

In this hands-on virtual lab, the participants will familiarize themselves with Deep Learning concepts and techniques, using MATLAB Online to train deep neural networks on GPUs in the cloud, create deep learning models from scratch for images and signal data, explore pretrained models and use transfer learning, import and export models from Python frameworks such as Keras and PyTorch, as well as automatically generate code for embedded targets. Deep Learning can achieve state-of-the-art accuracy when it comes to complex problems such as image classification or developing predictive models for signal processing applications. Deep Learning outperforms humans in some tasks like classifying objects in images. Cancer researchers are using deep learning to automatically detect cancer cells, for example.  This is an introductory class with a 3.5 hour duration, including a 20 minute break.