A rare disease (RD) is any disease that affects a small percentage of the population. In Europe a disease or disorder is defined as rare when it affects less than 1 in 2000 citizens. There are more than 7000 RDs worldwide. Although individually rare, collectively RDs are estimated to affect 350 million people globally. Most rare diseases are genetic and are present throughout a person's entire life, even if symptoms do not immediately appear. RDs are characterized by each having a wide diversity of symptoms, which can vary from patient to patient. Symptoms of RDs can also appear to be similar to those of common diseases. These factors mean that RDs can often be misdiagnosed.
According to the Global Genes organization, 8 out of 10 RDs are caused by a faulty gene and approximately 75% affect children, yet it takes an average of 4.8 years to arrive at an accurate diagnosis. This is part of the reason for 30% of children with RDs not living to see their fifth birthday. There are numerous challenges and issues that need to be addressed, ranging from technical to theoretical points of view, such as the small number of patients (often children), the heterogeneity of the disease, and the limited amount of national/international data resources.
The development of new technologies, such as genomic analysis by means of next generation sequencing (NGS) and other “omics technologies”, has boosted the molecular understanding and diagnosis of RDs. However, there is a growing need to develop new methods to integrate multi-omics data from different technologies. Furthermore, the ability of AI technologies to integrate and analyze data from different sources (e.g., multi-omics, patient registries) can be used to overcome further challenges, such as low diagnostic rates, reduced number of patients, and geographical dispersion. Ultimately AI-mediated knowledge could significantly boost therapy development for RDs.
The goal of this Research Topic is to publish original manuscripts that address broad challenges on both the application and theory of AI and multi-omics in the field of RD. Potential topics include, but are not limited to, the following:
• The application of AI in RD: how AI can solve/help RD diagnostic?
• AI approaches to solve RDs challenges such as low diagnostic rates, reduced number of patients, geographical dispersion, limited annotated data.
• Limitations of current AI approaches and how can we improve it
• Development or application of multi-omics methods for RD diagnosis
• Novel statistical methods for multi-omics data integration in RD
We welcome submissions of the following article types: Brief Research Report, Correction, Editorial, General Commentary, Hypothesis and Theory, Methods, Mini Review, Opinion, Original Research, Perspective, Review and Technology and Code.
A rare disease (RD) is any disease that affects a small percentage of the population. In Europe a disease or disorder is defined as rare when it affects less than 1 in 2000 citizens. There are more than 7000 RDs worldwide. Although individually rare, collectively RDs are estimated to affect 350 million people globally. Most rare diseases are genetic and are present throughout a person's entire life, even if symptoms do not immediately appear. RDs are characterized by each having a wide diversity of symptoms, which can vary from patient to patient. Symptoms of RDs can also appear to be similar to those of common diseases. These factors mean that RDs can often be misdiagnosed.
According to the Global Genes organization, 8 out of 10 RDs are caused by a faulty gene and approximately 75% affect children, yet it takes an average of 4.8 years to arrive at an accurate diagnosis. This is part of the reason for 30% of children with RDs not living to see their fifth birthday. There are numerous challenges and issues that need to be addressed, ranging from technical to theoretical points of view, such as the small number of patients (often children), the heterogeneity of the disease, and the limited amount of national/international data resources.
The development of new technologies, such as genomic analysis by means of next generation sequencing (NGS) and other “omics technologies”, has boosted the molecular understanding and diagnosis of RDs. However, there is a growing need to develop new methods to integrate multi-omics data from different technologies. Furthermore, the ability of AI technologies to integrate and analyze data from different sources (e.g., multi-omics, patient registries) can be used to overcome further challenges, such as low diagnostic rates, reduced number of patients, and geographical dispersion. Ultimately AI-mediated knowledge could significantly boost therapy development for RDs.
The goal of this Research Topic is to publish original manuscripts that address broad challenges on both the application and theory of AI and multi-omics in the field of RD. Potential topics include, but are not limited to, the following:
• The application of AI in RD: how AI can solve/help RD diagnostic?
• AI approaches to solve RDs challenges such as low diagnostic rates, reduced number of patients, geographical dispersion, limited annotated data.
• Limitations of current AI approaches and how can we improve it
• Development or application of multi-omics methods for RD diagnosis
• Novel statistical methods for multi-omics data integration in RD
We welcome submissions of the following article types: Brief Research Report, Correction, Editorial, General Commentary, Hypothesis and Theory, Methods, Mini Review, Opinion, Original Research, Perspective, Review and Technology and Code.
