COVID-19: Impacts and Insights from AI & Machine Learning Webinar
August 13, 2020 Thursday 10:00 AM-11:40 AM (Taipei Time)
The webinar will be conducted in English.Register here
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.
RNA Address Codes for Human and Viral Genomes
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. Prof. Chang 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.
Howard Chang , MD, PhD
Mapping COVID-19 Virus Mutations through Artificial Intelligence
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.
Dr. Ching-Yung Lin
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.
Adam Jia Kang Goh
Dr. Howard Y. Chang
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.
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Chang earned a Ph.D. in Biology from MIT, M.D. from Harvard Medical School, and completed Dermatology residency and postdoctoral training at Stanford University. His research addresses how large sets of genes are turned on or off together, which is important in normal development, cancer, and aging. Chang discovered a new class of genes, termed long noncoding RNAs, can control gene activity throughout the genome, illuminating a new layer of biological regulation. He invented ATAC-seq and other new methods for defining DNA regulatory elements genome-wide and in single cells. The long term goal of his research is to decipher the regulatory information in the genome to benefit human health.
Dr. Chang’s honors include the NAS Award for Molecular Biology, Outstanding Investigator Award of the National Cancer Institute, Paul Marks Prize for Cancer Research, Judson Daland Prize of the American Philosophical Society, and the Vilcek Prize for Creative Promise. He is a Member of the National Academy of Sciences, National Academy of Medicine, American Academy of Arts and Sciences, American Society for Clinical Investigation and Academia Sinica. His work was honored by the journal Cell as a Landmark paper over the last 40 years and by Science as “Insight of the decade”.
Dr. Ching-Yung Lin
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.
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Dr. Lin was named an IEEE Fellow in Nov 2011, the first in the area of Network Science. He is an author of 170+ publications and 29 awarded patents. In 2010, IBM Exploratory Research Career Review selected Dr. Lin as one of the five researchers "mostly likely to have the greatest scientific impact for IBM and the world.” His “Big Data Analytics” course at Columbia University attracts over 300 graduate students every year, and is the top search result on Baidu for Big Data Analytics. He led a team of ~40 researchers from Columbia University, CMU, Northeastern University, Northwestern University, UC Berkeley, Stanford Research Institute, Rutgers University, University of Minnesota and NMU in the then largest US social media analysis project from 2012 to 2015. He also led a project focusing on predicting human behavior for cognitive security applications.
In 2015, he was invited to be a panelist together with the White House Chief Data Scientist at the semi-annual conference of the American Medical Association. He was invited as a keynote or plenary speaker at 20+ conferences, including the International Conference on Cybersecurity hosted by the FBI in 2016 and the Expo 2.0 at the New York Javits Convention Center. His work has won seven best paper awards, been shown in 100+ press releases, and was featured four times by BusinessWeek magazine, including Top Story of the Week in May, 2009.
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.
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Based in Singapore, he collaborates with a team of professionals to help institutions solve their biggest data management problems.
Adam was previously from ExxonMobil as a Senior Key Account Manager, where he was responsible for driving direct and channel sales and opening new avenues of growth across all industries. Adam has a degree in Bachelor of Engineering (Honours) in Computer Engineering from Nanyang Technological University.