Data Dialogue
Thursday, September 21, 2017, 11:45am, Ahmadieh Family Grand Hall (Gross 330)
Carolyn Zhang (Duke BME)
Bacterial Strain Identification Using Temporal Growth Dynamics

Abstract:

As microbes develop numerous mechanisms to resist antibiotics, antibiotic resistance has become a global health crisis. As a result, rapid and accurate diagnostic techniques are required. Diagnosis of bacterial infections requires two main aspects – identification of the pathogen and its resistances. In both cases, current technology relies heavily on either qualitative assays or sequence dependent techniques. However, there exist numerous limitations with each of these approaches. To address these limitations, we examine the extent to which improving traditional microbiology methods can increase the diagnostic resolution of bacterial infections. To this end, we apply machine learning and the quantification of bacterial growth dynamics for the identification of bacterial strains in the clinic.

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