COVID-19: Impacts and Insights from AI & Machine Learning Webinar
Time : August 13, 2020 Thursday 10:00 AM-11:40 AM (Taipei Time)
The webinar will be conducted in English.
Link : Here
Introduction
Machine learning and AI are increasingly used for uncovering new insights into viral research. In this webinar genome-scale RNA localization of human and viral transcripts, such as for SARS-CoV-2 viral RNAs are discussed using APEX-seq, a machine learning method that quantifies RNA subcellular residency on a genome-wide scale. In the second half of the webinar the evolution and propagation of viruses using AI graph-based mapping techniques is described. Quick mapping of mutations is needed to identify targets for drug development and public health predictions. Elsevier will also share how we support the research community via the COVID-19 Research Collaboration Portal and finding datasets on Mendeley Data.
Agenda
| Time | Topic |
|---|---|
| 10:00AM - 10:40AM |
RNA address codes for human and viral genomes Howard Chang , MD, PhD In biology as in real estate, location is a cardinal organizational principle that dictates the accessibility and flow of informational traffic. An essential question in cell biology is the nature of the address code--how objects are placed and later searched for and retrieved. RNAs have emerged as key components of the address code, allowing protein complexes, genes, and chromosomes to be trafficked to appropriate locations and subject to proper activation and deactivation. I will discuss the development of APEX-seq, a method that quantifies RNA subcellular residency on a genome-wide scale. Genome-scale RNA localization data then propelled the development of computational models that can predict the RNA localization of human and viral transcripts, such as for SARS-CoV-2 viral RNAs. |
| 10:40AM - 11:20AM |
Mapping COVID-19 Virus Mutations through Artificial Intelligence Dr. Ching-Yung Lin Graphen, Inc., a startup building graphed based AI solutions, launched its AI gene evolution pathway analysis of the virus that causes the Coronavirus, COVID-19 on March 11, 2020. The team led by Dr. Ching-Yung Lin, AI big data analysis expert and Graphen founder in the United States, took only one week to map out the COVID-19 virus genes that have appeared so far. As of June 18, 2020, 22,402 different strains have been found from worldwide COVID-19 viruses distributed into eight categories. In this session, Dr. Lin will discuss how viruses evolve and propagate over time. Mapping mutations and propagation patterns can help companies better identify targets for drug development, public health predictions on virus spreading speed, or predict the harmfulness of specific variants that may cause symptoms beyond those observed from the original strain. |
| 11:20AM - 11:40AM |
Supporting Research Collaboration during COVID-19 Elsevier is committed to help researchers and life science companies accelerate efforts to address the COVID-19 global health crisis. We are pleased to offer the new Elsevier Coronavirus Research Hub, which currently includes a biomedical database, scientific and clinical content, COVID-19 specific datasets, a biomedically-focused text mining solution and several research collaboration tools. For this session, we would like to share in more detail how we support the research community via the COVID-19 Research Collaboration Portal and finding datasets on Mendeley Data. Adam Jia Kang Goh |
Speakers
Dr. Howard Y. Chang
Stanford University
Howard Y. Chang M.D., Ph.D. is Director of the Center for Personal Dynamic Regulomes and the Virginia and D.K. Ludwig Professor of Cancer Genomics at Stanford University. He is a Howard Hughes Medical Institute Investigator; he is also Professor of Dermatology and of Genetics at Stanford University School of Medicine.
Dr. Ching-Yung Lin
Graphen Inc.
Dr. Ching-Yung Lin is the CEO of Graphen, Inc., a startup focusing on developing next-generation Artificial Intelligence technologies, especially solutions for the Financial Services industry and Healthcare Industry. Before June 2017, he was an IBM Chief Scientist and an IBM Distinguished Researcher. He led the Network Science and Machine Intelligence department at IBM T. J. Watson Research Center. He is also an Adjunct Professor at Columbia University since 2005, an Affiliate Professor at the University of Washington from 2003 to 2009 and an Adjunct Professor at NYU in 2014.
Adam Goh
Elsevier
Adam joined Elsevier in 2014 and is responsible for the strategic direction of Elsevier’s SaaS solutions across the Asia Pacific region. This includes Pure, the leading Research Information Management System, as well as Mendeley Data, Elsevier’s latest research data management (RDM) solution.