CFP: AI-enabled Data Science for COVID-19, Frontiers in Big Data/Artificial Intelligence
Topic: AI-enabled Data Science for COVID-19
Journal: Frontiers in Big Data/Artificial Intelligence

COVID-19 is a pandemic that has spread all over the world. With the US now projected at over 6 million cases, and a lot more people are assumed to be exposed and asymptomatic, based on the seroprevalence studies. With the many COVID-19 related datasets that have been collected, AI is helping us fight this virus with applications such as early detection and diagnosis, contact tracing, projection of cases and mortality, development of drugs and vaccines, etc. We invite submission of papers describing timely and innovative research on all aspects of using AI in the fight against COVID.

We invite submission of papers describing timely and innovative research on fighting COVID-19 using AI. Some examples that have been delivered in our BIOKDD 2020 workshop ( include:

(i) bioinformatics (e.g., SARS-CoV-2 study using signature mutations and human leukocyte antigen)
(ii) data curation (e.g., COVID-19 knowledge graph and knowledge base, gene signature database, 1Point3Acres CovidNet, COVID-19 literature curation),
(iii) deep learning models (e.g., for case projection, COVID-19 detection using chest X-ray), and
(iv) statistical methods (e.g., analysis using Bayesian inference and virtual reality).

Keywords: Data Science, AI, Medicine, COVID-19, Bioinformatics

We welcome papers in all aspects of using AI in the fight against COVID-19, such as clinical, epidemiological, data-driven machine learning, statistical research in developing AI for COVID-19, as well as application-oriented papers that make innovative technical contributions for this fight against COVID-19. Submissions to this research topic can include but are not limited to:

- Bioinformatics approaches for sequence analysis and omics analysis of COVID-19
- Literature mining over COVID-19 publications
- Drug and vaccine development for COVID-19
- Medical imaging approaches for COVID-19 prognoses/diagnoses
- Epidemic monitoring and prediction of COVID-19 transmission
- Benchmarking of models and methods fighting COVID-19
- Case and contact tracking of COVID-19 infections and deaths
- Data integration, querying and sharing of COVID-19 related datasets
- COVID-19 related clinical data analysis
- Methods using data mining, machine learning (including deep learning) to fight COVID-19

Abstract Due: December 15, 2020
Manuscript Due: March 03, 2021

* Da Yan (University of Alabama at Birmingham)
* Hong Qin (University of Tennessee at Chattanooga)
* Hsiang-Yun Wu (Vienna University of Technology Vienna)
* Jake Chen (University of Alabama at Birmingham)

If you have questions, please do not hesitate to contact the organizers at