Online Lecture:COVID-19: Impacts and Insights from AI & Machine Learning Webinar - August 13

Poster:Hermia HoPost date:2020-07-30

Event Date & TimeAugust 13, 2020 (Thursday) 10:00 AM - 11:40 AM (Taipei Time)

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.

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Time Topic

RNA address codes for human and viral genomes

Howard Chang , MD, PhD
Virginia and D. K. Ludwig Professor of Cancer Genomics and of Genetics
Stanford University

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.


Mapping COVID-19 Virus Mutations through Artificial Intelligence

Dr. Ching-Yung Lin
Founder & CEO of Graphen Inc. and Adjunct Professor in Columbia University

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.


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
Regional Solution Sales Manager, Pure

Last modification time:2020-08-07 PM 4:32

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