- The use of data-driven approaches, such as machine learning, data mining, and big data analytics, in genetics and genomics research, including the analysis of high-throughput genomic data, high-throughput phenotyping data (deep phenotyping, phenomics), and multi-omics data.
- The development and application of novel computational methods, algorithms, and software tools for data integration, feature selection, network analysis, and visualization in genetics and genomics research.
- The investigation of genetic and genomic architecture, including the identification of causal variants, genes, and pathways, the analysis of gene expression regulation, epigenetics, and gene-environment interactions, and the study of population genetics and evolutionary genomics.
- The use of data intelligence in genetics and genomics research for the improvement of human health, animal and plant breeding, and agricultural productivity, including the identification of disease biomarkers, drug targets, and genetic traits of economic importance.
- The exploration of ethical, legal, and social issues related to data intelligence in genetics and genomics research, such as privacy, data ownership, consent, and equity.
Aims:
- To provide a high-quality platform for the dissemination of original research, reviews, and editorials that advance the field of data intelligence in genetics and genomics.
- To foster interdisciplinary collaborations and exchange of ideas among researchers from diverse fields, including biology, computer science, statistics, engineering, and social sciences.
- To promote the development and implementation of innovative data-driven approaches and computational tools for genetics and genomics research, including the integration of multi-modal data, the development of predictive models, and the interpretation of complex data.
- To facilitate the translation of data intelligence in genetics and genomics research into practical applications that benefit human health, animal and plant breeding, and agricultural productivity.
- To promote ethical and responsible data sharing and use practices in genetics and genomics research, and to engage with the public and policymakers to address the societal implications of data intelligence in genetics and genomics.