Harvard Medicine, formerly known as the Harvard Medical Alumni Bulletin, has been going strong since 1927. Published three times a year, the magazine captures the work of the Harvard Medical School research and alumni communities and illuminates their contributions to human health.
In the current issue, centered around the health hazards of climate disruption, Chirag Patel is among the HMS faculty profiled in After Effects, a piece about research into the impact of epigenetic mechanisms on genes and development. He and his team are working to find and replicate associations between environmental exposures and disease using human population data sets.
A Closer Read takes us on a deep dive into natural language processing (NLP), a specialty of Alexa McCray. She argues that the effectiveness of NLP is largely dependent on the quality of the source data. Through projects such as the Undiagnosed Diseases Network, she continues her career-long advocacy for providing the computational community with access to high-quality data. Peter Park and postdoctoral fellow Doga Gulhan are also using an approach drawn from NLP, scanning DNA mutation signatures in 2,700 different tumor genome sequences from the International Cancer Genome Consortium to identify causal factors.
Links to the two stories are below. Portaits by John Soares.
We’ve had some debatable evidence that some environmental exposures are correlated with changes in the epigenome, but I think what is missing in this field are methods to deduce causal connections.
—Chirag Patel, Assistant Professor of Biomedical
Informatics, interviewed in After Effects
Data standards, curation, and language processing, these are areas where I think we have to put more of our combined energy. Otherwise, it’s the Tower of Babel. What we need to get to is a point where we can compare apples to apples across biomedicine.
—Alexa McCray, Professor of Medicine,
interviewed in A Closer Read
Sequencing the genomics of cancer patients will soon be a routine practice, and this type of genome analysis will help us sift through the mutations that reflect the history of the tumor, so that we can identify the best drug or combination of drugs to use for the patient.
—Peter Park, Professor of Biomedical Informatics
If we think of each person’s genome as a book that contains many mutations or words, we can use our algorithms to find words that occur together and group them by common occurrences into broad topics. You cannot do this using only a few genomes. You need a big set of books so that you can determine what the topics are. Then you can look at each genome to see which topics it contains.
—Doga Gulhan, Research Fellow in Biomedical Informatics, interviewed in A Closer Read