Seminar Series: Introduction to Artificial Intelligence in Biological Data – NIH.AI Events
|Wednesday, September 22, 2021 @ 12:00 am EDT — Thursday, September 23, 2021 @ 12:00 am EDT|
|Are you interested in learning how to use artificial intelligence for analysis of biological data? Experts from NCI, Frederick National Laboratory for Cancer Research, Lawrence Livermore National Laboratory, and NVIDIA will provide real-world examples on using AI for 1) drug development, 2) image analysis, 3) molecular data, and 4) multimodal data. The one-hour seminars will be offered monthly from May 25, 2021 through September 23, 2021 and are hosted jointly by the NCI Bioinformatics Training and Education Program (BTEP) and the NCI Data Science Learning Exchange. See the descriptions, below for more information or contact NCIDataScienceLearningExchange@mail.nih.gov.
AI in Drug Development, presented by the ATOM consortium
Date/Time: May 25th, 2021, 1 – 2 pm ET
Presenter: Jonathan Allen PhD, Computational Scientist, Lawrence Livermore National Laboratory
Title: Building data-driven small molecule property prediction models with AMPL
Description: This talk will introduce basic concepts in building small molecule property prediction using machine learning models trained on data collected from experimental assays. Practical challenges will be considered, starting with limitations in data collection and curation through to model selection for property prediction applications. The ATOM (Accelerating Therapeutics for Opportunities in Medicine) Modeling Pipeline (AMPL) will be used to provide concrete examples for building models and data visualization.
AI in Image Analysis, presented by CCR: AIR and High Throughput Imaging Facility
Date/Time: June 15, 2021, 11 am – 12 pm ET
Meeting Link: https://cbiit.webex.com/cbiit/j.php?MTID=m8f1460fe5079163dd2a2a4e2641c227f
Presenters: Gianluca Pegoraro PhD (Staff scientist) , G Tom Brown MD/PhD (Staff clinician), Center for Cancer Research, NCI
Title: Overview of Deep Learning Applications in Bioimaging and Digital Pathology
Description: Deep learning is a subclass of machine learning which has shown excellent performance in learning tasks on unstructured data, such as digital images. This talk consists of two parts. In the first part , we will discuss some recent development of machine learning, with emphasis on the cellular object segmentation and tracking, image classification, and image restoration. The second part focuses on applications in digital pathology where we will talk about data acquisition, curation, and cleaning, as well as approaches to deep-learning. By the end of this part, you should be aware of potential pitfalls than confound results and have a better understanding of what it takes to carry out a deep-learning project in digital pathology.
AI in Molecular Data, presented by NVIDIA
Date/Time: July 15, 2021, 1 – 2 pm ET
Meeting Link: https://cbiit.webex.com/cbiit/j.php?MTID=m933690e1ccdbfd15ae3f75d1cbec3b95
Presenter: Avantika Lal PhD, Senior Scientist Deep Learning and Genomics NVIDIA
Title: Machine Learning Tools to Analyze Gene Expression and Regulation
Description: This talk will describe machine learning and deep learning methods to analyze bulk and single-cell RNA sequencing data, as well as deep learning models that integrate epigenetic data to decipher the regulatory networks underlying gene expression.
AI for Multimodal Data, presented by members of the Strategic and Data Science Initiatives, Frederick National Laboratory for Cancer Research.
Date/Time: Sep 23, 2021, 1 – 2 pm ET
Meeting Link: https://cbiit.webex.com/cbiit/j.php?MTID=m5fa0e43ae167ed5ea3a77fb25d339a82
Presenters: George Zaki, Bioinformatics Manager, Strategic and Data Science Initiatives (SDSI), Frederick National Laboratory for Cancer Research (FNL), Pinyi Lu, Bioinformatics analyst, SDSI, FNL
Title: Building Predictive Models From Multimodal Data Using Machine Learning
Description: In this talk, we will highlight two examples for building predictive models from multi modal data. The first example predicts dose response in cell lines based on drug and molecular features. The second example will show how to combine pathology from whole slide images and molecular features for cancer diagnosis and prognosis.
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