Near Unsupervised Learning for Early Discovery of Novel Patterns: Methods, Scalability and Label Dependability

这个项目的目的是使用现有的少量类标签来预测未知的类标签。该项目的成果与经济、环境、能源和卫生部门直接相关，因为这些领域提供了大量数据。例如，如果一个致癌基因，一个突变时可能导致癌症的基因，可以使用只有很少标签的数据和大量未标记的数据来发现，实验室实验所需的成本和时间可以大大减少，使制药公司能够更快地开发相应的药物。它不仅可以拯救更多的生命，还可以创造数百万美元的收入。

This project aims to predict the unknown class labels using the existing small number of class labels. The outcomes of the project have direct relevance to the economy, environment, energy and health sectors due to the abundance of data coming out of these areas. For example, if an oncogene, a gene that can cause cancer when mutated can be found using data with only few labels and a large amount of unlabelled data, the costs and time needed for lab experimentation can be greatly reduced enabling pharmaceutical companies to develop corresponding medicines quicker. It will not only save more lives but also generates millions of dollars of income.