When using machine learning for in silico modeling, the goal is normally to obtain highly accurate predictive models. Often, however, models should also bring insights into intere...
We present a novel approach to semisupervised learning which is based on statistical physics. Most of the former work in the field of semi-supervised learning classifies the point...
It is difficult to apply machine learning to new domains because often we lack labeled problem instances. In this paper, we provide a solution to this problem that leverages domai...
Background: Much recent work in bioinformatics has focused on the inference of various types of biological networks, representing gene regulation, metabolic processes, protein-pro...
Jean-Philippe Vert, Jian Qiu, William Stafford Nob...
Typical approaches to multi-label classification problem require learning an independent classifier for every label from all the examples and features. This can become a computati...