Professor Yongyong Shi, PhD, Shanghai Jiao Tong University, China
Hereditas is calling for submissions to our Collection on "Deep learning the genome".
Image credit: © libre de droit / Getty Images / iStock
The field of data mining is crucial for advancing our collective understanding of genetics and epigenetics, as it enables researchers to extract meaningful insights from vast and complex datasets. With the rapid growth of genomic data, the ability to analyze and interpret this information is more important than ever. Significant advances have already been made, such as the development of sophisticated algorithms that can identify genetic variants associated with diseases, and the integration of machine learning techniques to predict phenotypic outcomes.
Looking ahead, the potential for future advancements in data mining is immense. As technology continues to evolve, we may see the emergence of more refined predictive models that can personalize medicine, leading to tailored treatment strategies for individuals based on their unique genetic makeup. Additionally, the integration of multi-omics data—combining genomics, proteomics, and metabolomics—could provide a more holistic view of biological systems, paving the way for innovative solutions to complex health challenges. By continuing to advance our understanding in this area, we can unlock new possibilities for improving health outcomes and addressing pressing global issues.
We invite researchers to submit their work to this Collection, which will showcase research including but not limited to the following topics:
- Machine learning in genetics
- Genomic data integration
- Predictive modeling in cancer
- Non-coding RNA analysis
- Epigenetic data mining
- Big data in plant genetics
- Microbial genomics
- Data mining techniques in healthcare
There are currently no articles in this collection.
This Collection welcomes submission of original research and review articles. Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal.
Articles for this Collection should be submitted via our submission system, Snapp. Please, select the appropriate Collection title “Deep learning the genome" under the “Details” tab during the submission stage.
Articles will undergo the journal’s standard peer-review process and are subject to all the journal’s standard policies. Articles will be added to the Collection as they are published.
The Editors have no competing interests with the submissions which they handle through the peer-review process. The peer-review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.