Class Prediction from Disparate Biological Data Sources using an Iterative Multi-kernel Algorithm

Abstract. For many biomedical modelling tasks a number of di®erent types of data may in°uence predictions made by the model. An estab- lished approach to pursuing supervised learning with multiple types of data is to encode these di®erent types of data into separate kernels and use multiple kernel learning. In this paper we propose a simple iterative approach to multiple kernel learning (MKL), focusing on multi-class clas- si¯cation. This approach uses a block L1-regularization term leading to a jointly convex formulation. It solves a standard multi-class classi¯cation problem for a single kernel, and then updates the kernel combinatorial coe±cients based on mixed RKHS norms. As opposed to other MKL ap- proaches, our iterative approach delivers a largely ignored message that MKL does not require sophisticated optimization methods while keeping competitive training times and accuracy across a variety of problems. We show that the proposed method outperforms state-of-the-art results on an important protein fold prediction dataset and gives competitive performance on a protein subcellular localization task.