Abstract
Rheumatoid arthritis (RA) is a systemic, chronic and inflammatory disease of unknown etiology with genetic predisposition. The advent of new biological agents, as well as the more traditional disease-modifying antirheumatic drugs, has resulted in highly efficient therapies for reducing the symptoms and signs of RA; however, not all patients show the same level of response in disease progression to these therapies. These variations suggest that RA patients may have different genetic regulatory mechanisms. The extensive polymorphisms revealed in non-coding gene-regulatory regions in the immune system, as well as genetic variations in drug-metabolizing enzymes, suggest that this type of variation is of functional and evolutionary importance and may provide clues for developing new therapeutic strategies. Pharmacogenetics is a rapidly advancing area of research that holds the promise that therapies will soon be tailored to an individual patients genetic profile.
Keywords: Pharmacogenomic, rheumatoid arthritis, TNF, IL-1, cytokines, gene polymorphisms
Call for Papers in Thematic Issues
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, ...read more
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 ...read 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 ...read more
Integrating Machine Learning with Genome Science: Pioneering Developments and Future Directions
Integrating machine learning (ML) with genome science is driving transformative advancements in fields such as genomics, personalized medicine, and drug discovery. Genomic data is vast and complex, making traditional analysis methods inadequate for uncovering deep insights. Machine learning, particularly deep learning models like convolutional neural networks (CNNs) and recurrent neural ...read more
Related Journals
Related Books
Mind Maps in Clinical Biochemistry
A Treatise on Ecological Science
From SARS-CoV to MARS-CoV
Microbiology for ICAR NET: A Comprehensive Exam Preparation Guide
Decolorization by Thanatephorus Cucumeris Dec 1
Industrial Applications of Soil Microbes
The Future of Agriculture: IoT, AI and Blockchain Technology for Sustainable Farming
Handbook of Integrated Weed Management for Major Field Crops
Practical Biochemistry
Anticancer Drugs Sourced from Marine Life
4