Target identification from multi-omics data using systems biology and machine-learning approaches

该项目的目的是开发强大的可解释的人工智能方法，以挖掘大型多组学扰动数据集，以获得新的机制见解。特别是，我们有兴趣开发监督学习模型，预测化学扰动对单个基因和蛋白质的影响。使用机器学习方法，您将从化学扰动资源（例如，连接图）和疾病引起的扰动，以了解基本的生物学机制。

The aim of this project is to develop robust explainable AI methodologies to mine large multi-omics perturbation datasets for novel mechanistic insights. In particular, we are interested in developing supervised learning models that predict the effect of chemical perturbation on individual genes and proteins. Using machine-learning approaches, you will capture omics information from chemical perturbations resources (e.g., Connectivity Map) and disease-induced perturbations to understand fundamental biological mechanisms.