Lingxiao Zhang

Learn More
OBJECTIVE The complexity of interactions between genes and the environment is a major challenge for type 1 diabetes studies. Nuclear chromatin is the interface between genetics and environment and the principal carrier of epigenetic information. Because histone tail modifications in chromatin are linked to gene transcription, we hypothesized that histone(More)
We assessed whether epigenetic histone posttranslational modifications are associated with the prolonged beneficial effects (metabolic memory) of intensive versus conventional therapy during the Diabetes Control and Complications Trial (DCCT) on the progression of microvascular outcomes in the long-term Epidemiology of Diabetes Interventions and(More)
Different immune cells are expected to have unique, obligatory, and stable epigenomes for cell-specific functions. Histone methylation is recognized as a major layer of the cellular epigenome. However, the discovery of histone demethylases raises questions about the stability of histone methylation and its role in the epigenome. In this study, we used(More)
Social coding sites (e.g., Github) provide various features like Forking and Sending Pull-requests to support crowd-based software engineering. When using these features, a large amount of user behavior data is recorded. User behavior data can reflect developers preferences and interests in software development activities. Online service providers in many(More)
1 OBJECTIVE—The complexity of interactions between genes and the environment is a major challenge for type 1 diabetes studies. Nuclear chromatin is the interface between genetics and environment and the principal carrier of epigenetic information. Because histone tail modifications in chromatin are linked to gene transcription, we hypothesized that histone(More)
Anomaly detection in healthcare data like patient records is no trivial task. The anomalies in these datasets are often caused by mismatches between different types of feature, e.g., medications that do not match with the diagnoses. Existing anomaly detection methods do not perform well when detecting "mismatches" between multiple types of(More)
Knowledge of how diseases progress and transform is crucial for clinical decision making. Frequent pattern mining techniques, such as sequential pattern mining (SPM) algorithms, can automatically extract such knowledge from large collections of electronic medical records (EMR). However, EMR data are usually unorganized and highly noisy. Finding meaningful(More)
  • 1