ICIC 2023 Workshop

The purpose of workshops at ICIC 2023 is to provide a comprehensive forum on topics that will not be fully explored during the main conference and to encourage in-depth discussions of future technical and application issues in Intelligent Computing Community. The workshop will also provide a platform for researchers of Intelligent Computing Community to set an agenda and lay the foundation for future development in Intelligent Computing Community. The number of accepted papers at each Workshop should be not less than 8 ones.

All the authors for each Workshop must follow the guidelines in CALL FOR PAPERS to prepare your submitted papers.

Proposals for Workshop should be submitted in ELECTRONIC FORMAT by http://www.ic-icc.cn/2023/icg/index.asp at Workshop Session.

The 2nd International Workshop on Data Mining and Computing for Biomedicine
De-Shuang Huang, Chun-Hou Zheng,Junfeng Xia,Yansen Su

1.The 2nd International Workshop on Data Mining and Computing for Biomedicine

De-Shuang Huang
Tongji University

Chun-Hou Zheng
Anhui University
Email: zhengch99@126.com

Junfeng Xia
Anhui University
Email: jfxia@ahu.edu.cn

Yansen Su
Anhui University
Email: suyansen@ahu.edu.cn

Scope and Topics:
The increasing availability of large and complex biomedical data sets to the research community, triggers the need to develop more advanced and sophisticated data mining and computing techniques to exploit and manage these big data. To provide a forum for data miners, data scientists, and clinical researchers to share their latest investigations in applying data mining and computing techniques to biomedical data, we will organize the first international workshop on data mining and computing for biomedicine. This workshop covers the theory and application of data mining and computing techniques to analyze a wide variety of biomedical data sets that can potentially lead to significant advances in the field. The topics of interest include but are not limited to the following:

  • Computational genetics, genomics and proteomics
  • Pharmacogenomics data mining
  • Medical image data mining
  • Application of machine learning and deep learning methods to clinical data
  • Biological and clinical data analysis and integration for translational research
  • Classifying and clustering big data in electronic health records
  • Algorithms to speed up the analysis of big biomedical data