Shuang Wang’s Laboratory of Computational Methods largely focuses on Omics data such as genomics, epigenetics, transcriptomics, and microbiomics in order to use individual or integrated molecular profiles to identify patterns, to predict outcomes and to understand disease mechanisms.
Wang was among the first to study epigenetic data in depth and showed our epigenome changes as cell divides, an essential element in carcinogenesis. She has developed multiple analytical methods aimed at identifying patterns in epigenetic data and predicting individual events in the future.
More recently, she has developed an algorithm based on epigenetic data that can identify epigenetic field defects, which are molecular alterations that happen early in carcinogenesis and are important for early cancer detection. This powerful algorithm is especially useful in cancer screening when molecular changes are minimal and hard to detect.
Current Precision Prevention Work
In any discipline with big data, researchers are trying to identify associations and patterns in the data to predict individual events in the future.
Wang’s recent work using comprehensive patient electronic health records integratively aims to capture and quantify similarities between pairs of patients according to their comprehensive information. Similarity-based case identification can help stratify patients and lead to more precise diagnosis and more effective treatment choices.