With the unprecedented development of high-throughput sequencing (NGS), big data has been generated and utilized to explore the pathogenesis of various human diseases. How to dig out gold from the overwhelming amount of data is a challenging and vital question in medical science.
Machine learning, a successful methodology to extract knowledge from big data, has been widely used in medical studies to reveal underlying mechanisms, identify potential therapeutic targets, make predictions of prognosis, and so forth. Indeed, novel and powerful machine learning algorithms have come forth and been applied in the endocrine disease field and made great achievements. Furthermore, there is an increasing demand for integrative analysis to solve endocrine-related problems using traditional and advanced machine learning algorithms.
This Research Topic will focus on the use of machine learning in the diagnosis, prognosis, prevention and treatment of patients with endocrine disease.
We welcome submissions of Original Research and Reviews. With a focus on endocrine disease, possible sub-topics may include, but are not limited to:
• Novel models for improved risk stratification;
• New treatment strategies based on machine learning;
• New machine learning algorithms;
• Development of biological databases;
• Development of novel software and pipelines in data analysis.
With the unprecedented development of high-throughput sequencing (NGS), big data has been generated and utilized to explore the pathogenesis of various human diseases. How to dig out gold from the overwhelming amount of data is a challenging and vital question in medical science.
Machine learning, a successful methodology to extract knowledge from big data, has been widely used in medical studies to reveal underlying mechanisms, identify potential therapeutic targets, make predictions of prognosis, and so forth. Indeed, novel and powerful machine learning algorithms have come forth and been applied in the endocrine disease field and made great achievements. Furthermore, there is an increasing demand for integrative analysis to solve endocrine-related problems using traditional and advanced machine learning algorithms.
This Research Topic will focus on the use of machine learning in the diagnosis, prognosis, prevention and treatment of patients with endocrine disease.
We welcome submissions of Original Research and Reviews. With a focus on endocrine disease, possible sub-topics may include, but are not limited to:
• Novel models for improved risk stratification;
• New treatment strategies based on machine learning;
• New machine learning algorithms;
• Development of biological databases;
• Development of novel software and pipelines in data analysis.
