Genomics, proteomics, metabolomics, transcriptomics—rapid advances in high-throughput biomedical technologies has enabled the collection of data with unprecedented detail from the growing number of omics. But, how best to take advantage of the interactions and complementary information in omics data?
To fully exploit the advances in omics technologies to achieve a more comprehensive understanding of the biological processes underlying human diseases, researchers from Regenstrief Institute and Indiana, Purdue and Tulane Universities have developed and tested MOGONET, a novel multi-omics data analysis algorithm and computational methodology. Integrating data from various omics provides a more holistic view of biological processes underlying human diseases. The creators have made MOGONET open source, free and accessible to all researchers… Continue reading.
Scientists recognize the connection between maternal obesity and liver cancer in the offspring of obese mothers, however, the mechanism is not well understood. In a novel study, appearing in the Journal of Hepatology, investigators have identified a microRNA in obese mouse mothers that appears to pass on liver cancer susceptibility, increasing the odds of liver cancer developing in their offspring and throughout future generations.
One third of the world population is overweight or obese and this global obesity epidemic is threatening human health. Obesity confers a higher risk of developing metabolic diseases such as non-alcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC). Up to 50 percent of recently diagnosed HCC in the United States is the result of hepatic metabolic disorders such as NAFLD… Continue reading.
Patients with chronic diseases such as breast cancer or colorectal cancer often experience fatigue, pain, depression and other symptoms which can lead to distress and functional impairment if left untreated. With the ultimate goal of helping clinicians manage and treat symptoms that negatively affect health and quality of life, researchers from the Regenstrief Institute and IUPUI have devised and tested novel methodologies to extract data on symptoms from electronic health records (EHRs) and have successfully investigated associations between symptom clusters and disease.
“Our novel methods can be generalized beyond breast and colorectal cancer to analyze symptom clusters of other chronic diseases where symptom management and treatment is critical,” said Regenstrief Institute investigator Kun Huang, Ph.D., senior author of the study and an internationally recognized leader in translational bioinformatics. “Identifying and understanding symptom clusters—which symptoms tend to go together—fatigue and depression, for example—and when these symptoms occur during the course of treatment—provides critical information to a patient’s care team, especially as we look forward to precision health and try to find the right treatment for the right patient at the right time… Continue reading.
WASHINGTON, D.C.—The American Institute for Medical and Biological Engineering (AIMBE) has announced the induction of Kun Huang, Ph.D., Professor of Medicine, Professor of Computer Science, IUSM; PHI Chair for Genomic Data Science, Director of Data Science and Informatics, Precision Health Initiative, Assistant Dean for Data Science, Department of Medicine/Division of Hematology and Oncology, Indiana University School of Medicine, to its College of Fellows. Dr. Huang was nominated, reviewed, and elected by peers and members of the College of Fellows for outstanding work in digital pathology, integrative genomics and leadership in translating data science and informatics techniques to precision medicine.