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Current Genomics
ISSN: 1389-2029 (Print)
eISSN: 1875-5488 (Online)
Special Issues With Active Call for Papers
Genomic Insights into Oncology: Harnessing Machine Learning for Breakthroughs in Cancer Genomics.
This special issue aims to explore the cutting-edge intersection of genomics and oncology, with a strong emphasis on original data and experimental validation. While maintaining the focus on how machine learning and advanced data analysis techniques are revolutionizing our understanding and treatment of cancer, this issue will prioritize contributions that go beyond mere data-mining. We seek to include comprehensive review articles that synthesize recent advancements and research papers that present novel data, demonstrating clear experimental... see more
Pathogen genomics & human health
Pathogens establish successful infection need to escape host immune surveillance. Alteration and mutation of genomics in pathogens confer advantages to subvert the host immune barrier. In this issue, we will collect and discuss current understanding in the field of “pathogen genomics and human disease”. see more
Current Genomics in Cardiovascular Research
Cardiovascular diseases are the main cause of death in the world, in recent years we have had important advances in the interaction between cardiovascular disease and genomics. In this Research Topic, we intend for researchers to present their results with a focus on basic, translational and clinical investigations associated with genomics and cardiovascular disease. Experimental studies, network meta-analysis, randomized controlled trials, systematic reviews and meta-analysis, umbrella reviews, meta-epidemiologic studies, cross-sectional studies, diagnosis, treatment, management, prevention... see more
Integrating Artificial Intelligence and Omics Approaches in Complex Diseases
Recent advancements in AI and omics methodologies have revolutionized the landscape of biomedical research, enabling us to extract valuable information from vast amounts of complex data. By combining AI algorithms with omics technologies such as genomics, proteomics, metabolomics, and transcriptomics, researchers can obtain a more comprehensive and multi-dimensional analysis of biological systems, ultimately leading to a better understanding of disease mechanisms. Complex diseases, characterized by their heterogeneous nature, pose unique challenges to researchers and clinicians... see more
Plant-Host Interactions and diversity: Advancements through Artificial Intelligence and Genomic
The thematic issue, titled "Plant-Host Interactions and Diversity: Advancements through Artificial Intelligence and Genomics," aims to bring together state-of-the-art research that explores the intersection between plant biology, host interactions, genomics, and artificial intelligence. The focus will be on how advancements in AI and genomics are revolutionizing our understanding of the complex interactions between plants and their associated hosts, be it mutualistic or parasitic. This special issue will provide a platform for researchers, academics, and industry... see more
Advanced AI Techniques in Big Genomic Data Analysis
The thematic issue on "Advanced AI Techniques in Big Genomic Data Analysis" aims to explore the cutting-edge methodologies and applications of artificial intelligence (AI) in the realm of genomic research, where vast amounts of data pose both challenges and opportunities. This issue will cover a broad spectrum of AI-driven strategies, including machine learning and deep learning models, tailored for extracting, analyzing, and interpreting complex patterns within large-scale genomic datasets. Contributions to this issue will highlight... see more
Deep learning in Single Cell Analysis
The field of biology is undergoing a revolution in our ability to study individual cells at the molecular level, and to integrate data from multiple sources and modalities. This has been made possible by advances in technologies for single-cell sequencing, multi-omics profiling, spatial transcriptomics, and high-throughput imaging, as well as the development of powerful machine learning and deep learning algorithms. In particular, the ability to analyze spatially resolved single-cell data has opened new avenues for... see more
Microbial Destruction of Resistant Pollutants: Adaptive Reactions, Genetic Determinants and Characteristics of Enzyme Systems
Microbial technologies for environmental removal of persistent pollutants are effective and low-cost. Microorganisms catalyze a huge variety of metabolic pathways for the destruction of toxicants, which allows them to adapt to almost any type of organic pollutant and use it as a substrate/cosubstrate. Modern research methods, such as NGS, transcriptomic analysis, and whole-genome sequencing, make it possible to study in detail the capabilities of a particular organism. However, without studying the properties of isolated enzymes,... see more
Applications of Single-cell Sequencing Technology in Reproductive Medicine
Single cell sequencing (SCS) technology utilizes individual cells' genetic material to sequence their genome, transcriptome, and epigenetics at the molecular level. It offers insights into cell heterogeneity and enables the study of limited biological materials. Since its recognition as a valuable technique in 2011, single cell sequencing has yielded numerous research findings. Recent advancements have rapidly expanded its applications, particularly in studying reproductive cells and embryonic stem cells with small sample sizes. These developments have... see more
Advanced Computational Algorithms and Artificial Intelligence in Clinical Pharmacogenomics
In the era of personalized medicine, understanding the relationship between genetics and drug response is crucial. This issue delves into innovative methodologies, leveraging deep computational analysis and artificial intelligence, to enhance the field of Clinical Pharmacogenomics. The interdisciplinary approach harnesses the power of advanced high-throughput genotyping technologies, sophisticated computational analysis, and machine learning algorithms to unravel the complexities of genotype-phenotype correlations. By scrutinizing vast genomic datasets with precision and speed, researchers can now identify genetic... see more