Large databases are created by genomics for the discovery, study, and development of novel treatments all around the world. It's not hard to conceive that artificial intelligence (AI) might currently study the 3 billion base pairs that make up humanoid genetic makeup in order to uncover genetic disparities among the population. By 2026, large pharmaceutical companies hope to have researched up to 2 million genomes and analyzed massive amounts of patient data from clinical drug studies. As new equipment is introduced, AI will be employed in genomics for a variety of omics investigations, including transcriptomics. To aid in the classification of potentially clinically significant genes, AI is used to combine data from genomic research with literature analysis. Machine learning is now a critical component of the genomics industry's growth. AI and Machine learning in genomics is already having an impact on a number of areas, including genetic testing, medical care delivery, and genomics accessibility for people interested in learning more about how their genes influence their health. The purpose of this research is to explore AI and Machine learning applications in gene technology and their roles in paving the way for future genomics machine learning applications.
The purpose of this research topic is to explore the possibilities for preparing highly effective drugs with clinical outcomes that greatly exceed standard therapies with the help of emerging technologies such as AI and ML. This Research Topic will also focus on the significant problems in the clinical research processes which are publishing the results of most traditional clinical studies on average treatment effects. The Research Topic also aim at AI's future potential in individualized medicine applications, particularly for risk prediction in common complex diseases, as well as the challenges, limitations, and biases that must be carefully addressed for AI to be successfully deployed in medical applications, particularly those utilizing human genetics and genomics data. The research area will also include the AI’s role for risk prediction in common complicated diseases and biases that must be properly addressed for individualized medicine to be successfully deployed.
The Research Topic will concentrate on AI-enabled technologies (such as machine learning and deep learning) that aid in the development of clinical diagnostic algorithms, which often require mapping a given diagnostic task to a broader problem class.
• The application of AI (deep learning, machine learning) in genomic diagnosis
• The applications of artificial intelligence in clinical genomic.
• DNA sequencing using AI
• Deep Genomics using machine learning
• Variant calling using machine learning
• Genome annotation and variant classification
• Phenotype-to-genotype correspondence
• Genotype-to-phenotype predictions
• AI in Neurodevelopmental disorders (NDDs)
• Image to genetic diagnosis
• Artificial Intelligence in genetics research and medicine
• Machine learning applications for therapeutic tasks
• Artificial intelligence and machine learning in precision and genomic medicine
• Artificial Intelligence and machine learning in medicinal
• Chemistry and validation of emerging drug targets
Large databases are created by genomics for the discovery, study, and development of novel treatments all around the world. It's not hard to conceive that artificial intelligence (AI) might currently study the 3 billion base pairs that make up humanoid genetic makeup in order to uncover genetic disparities among the population. By 2026, large pharmaceutical companies hope to have researched up to 2 million genomes and analyzed massive amounts of patient data from clinical drug studies. As new equipment is introduced, AI will be employed in genomics for a variety of omics investigations, including transcriptomics. To aid in the classification of potentially clinically significant genes, AI is used to combine data from genomic research with literature analysis. Machine learning is now a critical component of the genomics industry's growth. AI and Machine learning in genomics is already having an impact on a number of areas, including genetic testing, medical care delivery, and genomics accessibility for people interested in learning more about how their genes influence their health. The purpose of this research is to explore AI and Machine learning applications in gene technology and their roles in paving the way for future genomics machine learning applications.
The purpose of this research topic is to explore the possibilities for preparing highly effective drugs with clinical outcomes that greatly exceed standard therapies with the help of emerging technologies such as AI and ML. This Research Topic will also focus on the significant problems in the clinical research processes which are publishing the results of most traditional clinical studies on average treatment effects. The Research Topic also aim at AI's future potential in individualized medicine applications, particularly for risk prediction in common complex diseases, as well as the challenges, limitations, and biases that must be carefully addressed for AI to be successfully deployed in medical applications, particularly those utilizing human genetics and genomics data. The research area will also include the AI’s role for risk prediction in common complicated diseases and biases that must be properly addressed for individualized medicine to be successfully deployed.
The Research Topic will concentrate on AI-enabled technologies (such as machine learning and deep learning) that aid in the development of clinical diagnostic algorithms, which often require mapping a given diagnostic task to a broader problem class.
• The application of AI (deep learning, machine learning) in genomic diagnosis
• The applications of artificial intelligence in clinical genomic.
• DNA sequencing using AI
• Deep Genomics using machine learning
• Variant calling using machine learning
• Genome annotation and variant classification
• Phenotype-to-genotype correspondence
• Genotype-to-phenotype predictions
• AI in Neurodevelopmental disorders (NDDs)
• Image to genetic diagnosis
• Artificial Intelligence in genetics research and medicine
• Machine learning applications for therapeutic tasks
• Artificial intelligence and machine learning in precision and genomic medicine
• Artificial Intelligence and machine learning in medicinal
• Chemistry and validation of emerging drug targets
