Researchers from Harvard University and Novartis have developed a data visualization tool called Peax that helps researchers find patterns in sequential data, including genomic data. The size and complexity of genomic data can make it difficult to identify patterns, and Peax allows users to search through their data based on visual descriptions of patterns to spot them. Peax also uses deep learning to improve over time as users label the patterns Peax returns as interesting or not interesting.
Visualizing Patterns in Sequential Data
Michael McLaughlin is a research assistant at the Center for Data Innovation. He researches and writes about a variety of issues related to information technology and Internet policy, including digital platforms, e-government, and artificial intelligence. Michael graduated from Wake Forest University, where he majored in Communication with Minors in Politics and International Affairs and Journalism. He received his Master’s in Communication at Stanford University, specializing in Data Journalism.
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