[Ieee_vis] CFP: AI-enabled Data Science for COVID-19

[Hsiang-Yun WU]

Dear Colleagues,

If you are looking for a journal to submit visual analytics approaches
specifically for COVID-19, maybe you would like to consider this
journal.

Topic: AI-enabled Data Science for COVID-19
Journal: Frontiers in Big Data/Artificial Intelligence
https://www.frontiersin.org/research-topics/16237/ai-enabled-data-science-for-covid-19


INTRODUCTION
------------
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
(http://home.biokdd.org/biokdd20/program.html) 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


TOPICS OF INTEREST
------------------
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

Other classical topics relevant to COVID-19 are also welcome.

- Bioimage analysis, single-cell analysis
- Biological network visualization
- Information visualization and visual analytics for biomedical data
- Development of deep learning methods for biological and clinical data
- Novel methods and frameworks for mining and integrating big biological data
- Discovering biological networks and pathways underlying biological
processes and diseases
- Analysis, discovery of biomarkers and mutations, and disease risk assessment
- Comparative genomics
- Metagenome analysis using sequencing data
- RNA-seq and microarray-based gene expression analysis
- Genome-wide analysis of non-coding RNAs
- Genome-wide regulatory motif discovery
- Structural bioinformatics
- Automated annotation of genes and proteins
- Discovery of structural variations from next-generation sequencing (NGS) data
- Correlating NGS with proteomics data analysis
- Discovery of genotype-phenotype associations
- Building predictive models for complex phenotypes
- Functional annotation of genes and proteins
- Cheminformatics
- Special biological data management techniques
- Privacy and security issues in mining genomic databases
- Predictive modeling for personalized treatment
- Semantic web and ontology-driven data integration methods
- Text mining for biomedical literature and clinical notes
- Information retrieval for healthcare and Biomedical applications
- Biomedical signal analysis and processing
- Intelligent medical data management
- Collaboration technologies for biomedicine
- Social networks for biomedicine


IMPORTANT DATES
---------------
Abstract Due: December 15, 2020
Manuscript Due: March 03, 2021


TOPIC EDITORS
-------------
* 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)


CONTACT
-------
If you have questions, please do not hesitate to contact the
organizers at yanda at uab.edu

Hsiang-Yun  (on behalf of the organizers)

-- 
Hsiang-Yun WU

e-mail: wu (at) cg.tuwien.ac.at
phone: +43-1-58801-18602 ext. 186206
fax: +43-1-58801-18698
URL: https://www.cg.tuwien.ac.at/staff/HsiangYunWu.html
URL2: http://yun-vis.net/