Tutorials
The role of the tutorials is to provide a platform for a more intensive scientific exchange amongst researchers interested in a particular topic and as a meeting point for the community. Tutorials complement the depth-oriented technical sessions by providing participants with broad overviews of emerging fields. A tutorial can be scheduled for 1.5 or 3 hours.
Tutorial proposals are accepted until:
January 17, 2025
If you wish to propose a new Tutorial please kindly fill out and submit this
Expression of Interest form.
Tutorial on
Enrich Data Analytics with GenAI
Instructor
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Stefan Helfrich
KNIME
Germany
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Brief Bio
Dr. Stefan Helfrich is responsible for academic relations at KNIME. Before, he worked as a Bioimage Analyst at the University of Konstanz, supporting users of the local light microscopy facility with image and data analysis tasks. Already during that time, he realized that it is crucial to build up the right set of skills for researchers, becoming his major motivation to teach data literacy skills (using KNIME).
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Abstract
Learn how to enrich data analytics with GenAI without writing a line of code. During this tutorial, we will explore an outlier detection use case and exploit the multi-language capabilities of GenAI to automatically generate custom alert messages.
You will become familiar with the free low-code tool KNIME Analytics Platform, techniques for outlier detection, and the use of GenAI via the KNIME AI Extension (Labs). We will discuss multiple GenAI services and providers, including OpenAI, GPT4All, Hugging Face, Chroma, FAISS, and more.
Keywords: Data Analytics, Low-Code, GenerativeAI, Outlier Detection, Data Apps
Aims and Learning Objectives:
- Learn how to build workflows with KNIME Analytics Platform
- Detect outliers visually and using statistical techniques - Prompt engineer an LLM via the OpenAI integration
- Create a vector store from a corpus of different types of documents (the knowledge base)
- Build and deploy a data app to display alerts and make it available to users on the web
Target Audience: Users who are interested in low-code tools for data analytics.
Prerequisite Knowledge of Audience: No coding is required. Please bring your laptop with KNIME Analytics Platform and the KNIME AI Extension (Labs) already installed.
Tutorial on
Advancing Healthcare by Discovering New Biomarkers Using AI Tools and Network Models
Instructor
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Hesham Ali
University of Nebraska at Omaha
United States
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Brief Bio
Hesham H. Ali is a Professor of Computer Science and the director of the University of Nebraska Omaha (UNO) Bioinformatics Core Facility. He served as the Lee and Wilma Seemann Distinguished Dean of the College of Information Science and Technology at UNO between 2006 and 2021. He has published numerous articles in various IT areas, including scheduling, distributed systems, data analytics, wireless networks, and Bioinformatics. He has been serving as the PI or Co-PI of several projects funded by NSF, NIH and Nebraska Research Initiative in the areas of data analytics, wireless networks and Bioinformatics. He has also been leading a Research Group that focuses on developing innovative computational approaches to model complex biomedical systems and analyze big bioinformatics data.
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Abstract
Abstract
The last several years witnessed major advancements in the development of technologies with the goal of collecting various types of data in many application domains. The biomedical domain represents a clear example of such development. In addition, every time the continuously evolving biomedical technologies make it possible for bioscience researchers to have access to new type of biological data, exciting research questions attract new studies: Would it possible for the new data to provide novel biological signals or biomarkers that can be used for supporting biomedical research and advancing healthcare? Would the biological signals or biomarkers associated with the new data be robust enough to be used for the purpose of early disease diagnosis or the assessment of different treatments for certain health conditions.
This tutorial begins with a brief introduction to biomarkers and how various biomedical technologies helped produce biomarkers that have significantly impacted biomedical research and healthcare. We then discuss how recent technologies have made it possible to generate new types of data that can be used to obtain a new generation of biomarkers. We discuss biomarkers obtained from mobile wearable devices, microbiome data, and nanoparticles profiles. We discuss how the exciting new AI tools and network models can leverage the new biomarkers in supporting biomedical research and lead to the next generation of healthcare.