mLASSO-Hum: A LASSO-based interpretable XXXX -protein subcellular localization predictor.

@entity1 @entity17031 @entity74
UNASSIGNED: Knowing the subcellular compartments of @entity1 proteins is essential to shed light on the mechanisms of a broad range of @entity1 diseases. In computational methods for protein subcellular localization, knowledge-based methods (especially @entity74 ( @entity74 ) based methods) are known to perform better than sequence-based methods. However, existing @entity74 -based predictors often lack interpretability and suffer from overfitting due to the high dimensionality of feature vectors. To address these problems, this paper proposes an interpretable multi-label predictor, namely mLASSO-Hum, which can yield sparse and interpretable solutions for large-scale prediction of @entity1 protein subcellular localization. By using the one-vs-rest @entity17031 -based classifiers, 87 out of more than 8000 @entity74 terms are found to play more significant roles in determining the subcellular localization. Based on these 87 essential @entity74 terms, we can decide not only where a protein resides within a cell, but also why it is located there. To further exploit information from the remaining @entity74 terms, a method based on the @entity74 hierarchical information derived from the depth distance of @entity74 terms is proposed. Experimental results show that mLASSO-Hum performs significantly better than state-of-the-art predictors. We also found that in addition to the @entity74 terms from the cellular component category, @entity74 terms from the other two categories also play important roles in the final classification decisions. For readers*  convenience, the mLASSO-Hum server is available online at http://bioinfo.eie.polyu.edu.hk/mLASSOHumServer/.
