ISSN Print: 2689-3967
ISSN Online: 2689-3975
Call for Papers
The Journal of Machine Learning for Modeling and Computing (JMLMC) is seeking submissions from leaders in the field. If you would like to contribute, please submit your articles in Begell House submission site at Begell House Submission System
Author instructions for the Journal of Machine Learning for Modeling and Computing can be found at: Instruction.pdf.
As part of the community reciprocation that furthers research in any field, authors who submit articles to JMLMC acknowledge that they may be asked to review other articles for the journal.Enquiries can be directed to Editor-in-Chief Dongbin Xiu at email@example.com.
Call for Papers: Computational Modeling and Machine Learning Applications to Biological, Bio-inspired, and Epidemiological Systems
Mathematical Sciences Department
George Mason University
Fairfax, VA, USA
Computer Science Department
Providence, RI, USA
This special issue is devoted to sharing the development and application of new computational tools for discovering novel biological phenomena, rules, and theories. Specifically, articles that employ machine learning to study biological, bio-inspired, and epidemiological systems to build informative and predictive models of the underlying processes are invited. We hope this issue will encourage researchers to collaborate in biological investigations using novel machine learning tools to guide the exploration and discovery of new rules, phenomena, and theories in living systems.
Applications of machine learning to the following topics are welcome (but not limited to):
1. Multiscale modeling and data integration for biological systems
2. Applications to regulatory, structural, and functional genomics
3. Modeling epidemiological data to improve our understanding of public health outcomes
4. Complex biological systems at the molecular and cellular scales
5. Modeling and control of multiphysics describing biological phenomena
6. Investigating complex patterns and processes in ecology and evolutionary biology
7. Advancing novel multidisciplinary educational curriculum, training, and collaborations
Submission Deadline: December 16, 2022