Machine learning

Traditional analytics tools may not be suited to capturing the full value of big data. The volume of data is too large for comprehensive analysis, and the potential relationships among disparate data sources are too complicated to derive all the value buried in the data.

Machine learning is a method of data analysis that automates analytical modeling, feature selection and predictive analytics. We have experience in applying support vector machine (SVM) models to genomic questions such as discovery of de novo mutations and distant regulatory elements, and tuning SVM models for heterogeneous data and avoid overfitting.

In combination with other analytical techniques that we provide, the best value can be generated out of the data.