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Photo by Darren Baker
Researchers say they have designed an online tool that can predict the role of proteins and genes involved in immunological diseases and processes.
The tool uses information compiled from 38,088 public experiments to predict new immune pathway interactions, mechanisms, and disease-associated genes.
Details on this publicly available tool, known as ImmuNet, were recently published in Immunity.
“This new tool unlocks the insight contained in big data, the world’s biomedical research output, to help understand immunological mechanisms and diseases,” said Stuart Sealfon, MD, of Mount Sinai Health System in New York, New York.
“The goal of ImmuNet is to accelerate the understanding of immune pathways and genes, ultimately leading to the development of improved treatment for diseases with an immunological component.”
ImmuNet enables immunology researchers without special computational training to use the statistical techniques of Bayesian data integration and machine learning algorithms to “interrogate” this compendium of public data.
“We expect the applicability of ImmuNet to wide-ranging areas of immunology will grow with the incorporation of continually increasing public big data,” said Olga Troyanskaya, PhD, of Princeton University in New Jersey.
“By enabling immune researchers from diverse backgrounds to leverage these valuable and heterogeneous data collections, ImmuNet has the potential to accelerate discovery in immunology.” 
Photo by Darren Baker
Researchers say they have designed an online tool that can predict the role of proteins and genes involved in immunological diseases and processes.
The tool uses information compiled from 38,088 public experiments to predict new immune pathway interactions, mechanisms, and disease-associated genes.
Details on this publicly available tool, known as ImmuNet, were recently published in Immunity.
“This new tool unlocks the insight contained in big data, the world’s biomedical research output, to help understand immunological mechanisms and diseases,” said Stuart Sealfon, MD, of Mount Sinai Health System in New York, New York.
“The goal of ImmuNet is to accelerate the understanding of immune pathways and genes, ultimately leading to the development of improved treatment for diseases with an immunological component.”
ImmuNet enables immunology researchers without special computational training to use the statistical techniques of Bayesian data integration and machine learning algorithms to “interrogate” this compendium of public data.
“We expect the applicability of ImmuNet to wide-ranging areas of immunology will grow with the incorporation of continually increasing public big data,” said Olga Troyanskaya, PhD, of Princeton University in New Jersey.
“By enabling immune researchers from diverse backgrounds to leverage these valuable and heterogeneous data collections, ImmuNet has the potential to accelerate discovery in immunology.”
Photo by Darren Baker
Researchers say they have designed an online tool that can predict the role of proteins and genes involved in immunological diseases and processes.
The tool uses information compiled from 38,088 public experiments to predict new immune pathway interactions, mechanisms, and disease-associated genes.
Details on this publicly available tool, known as ImmuNet, were recently published in Immunity.
“This new tool unlocks the insight contained in big data, the world’s biomedical research output, to help understand immunological mechanisms and diseases,” said Stuart Sealfon, MD, of Mount Sinai Health System in New York, New York.
“The goal of ImmuNet is to accelerate the understanding of immune pathways and genes, ultimately leading to the development of improved treatment for diseases with an immunological component.”
ImmuNet enables immunology researchers without special computational training to use the statistical techniques of Bayesian data integration and machine learning algorithms to “interrogate” this compendium of public data.
“We expect the applicability of ImmuNet to wide-ranging areas of immunology will grow with the incorporation of continually increasing public big data,” said Olga Troyanskaya, PhD, of Princeton University in New Jersey.
“By enabling immune researchers from diverse backgrounds to leverage these valuable and heterogeneous data collections, ImmuNet has the potential to accelerate discovery in immunology.”