Project: Genomic and environmental predictor of preterm birth
Predicting preterm birth in nulliparous women is challenging and our efforts to develop predictors for that condition from environmental variables produce insufficient classifier accuracy. Recent studies highlight the involvement of common genetic variants in length of pregnancy. This project involves the development of a risk score for preterm birth based on both genetic and environmental attributes.
This project is sponsored by DSI Center for Health Analytics.
- Professors Itsik Pe’er and Ansaf Salleb-Aouissi
- Department/School: Computer Science/SEAS
- Location: Computer Science Building
- Start date: 06/05/2018
- End date: 08/10/2018
- Number of hours per week of research expected: 40
- Skill sets: Python/scikit learn; machine learning
- Student eligibility (as of Spring 2018):
freshman, sophomore, junior, senior, master’s
- International students on F1 or J1 visa: eligible