Drs. Zeeshan Ahmed, Saman Zeeshan, and Donghyung Lee has edited and published a new Research Topic, “Artificial Intelligence for Personalized and Predictive Genomics Data Analysis“, in the Section of Computational Genomics, Frontiers in Genetics.
The quest to understand what causes chronic, acute, and infectious diseases has been a central focus of human health studies since the beginning of scientific discovery. Our evolving understanding of their complex nature has led us to realize the importance of effective diagnosis and treatment of patients with these conditions. Over the last few decades, genomics has been leading us towards an audacious future; it has been changing our views about conducting biomedical research, studying diseases, and understanding diversity in our society across the human species. However, there are more unknowns than knowns in genomics. By identifying the novel risk factors and disease biomarkers, genomics and precision medicine has the potential to translate scientific discovery into clinically actionable personal healthcare. Nevertheless, we still require innovative and intelligent solutions to advance genomics and precision medicine such as creating new models of medicine where physicians use clinical decision support systems based on Artificial Intelligence (AI) to choose the best treatment for a patient guided by the genomics variants that each of us have.
The goal of this Research Topic is to publish Findable, Accessible, Intelligent, Reproducible (FAIR), and related approaches proposed to facilitate implementation of genomics and precision medicine, and accelerate diagnostic and preventive care delivery strategies beyond traditional symptom-driven, disease-causal medical practice. This special issue features Artificial Intelligence, Machine Learning, Genomics and Precision Medicine, and invites relevant high-quality original research articles, reviews, perspectives, and opinions for transparent peer review and publication.
Editorial:
- Ahmed, Z. Zeeshan, S., & Lee, D. (2023). Editorial: Artificial intelligence for personalized and predictive genomics data analysis. Frontiers in Genetics | Computational Genomics. 14:1162869. PMID: 36936434. (Frontiers)
Featured and published articles of the research topic:
- TLsub: A transfer learning based enhancement to accurately detect mutations with wide-spectrum sub-clonal proportion.
- Molecular subtypes based on cuproptosis regulators and immune infiltration in kidney renal clear cell carcinoma.
- Development and validation of a chromatin regulator prognostic signature in colon adenocarcinoma.
- SPCMLMI: A structural perturbation-based matrix completion method to predict lncRNA–miRNA interactions.
- Artificial Intelligence, Healthcare, Clinical Genomics, and Pharmacogenomics Approaches in Precision Medicine.