AUC-Maximized Deep Convolutional Neural Fields for Protein Sequence Labeling

Conference paper

DOI: 10.1007/978-3-319-46227-1_1

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9852)
Cite this paper as:
Wang S., Sun S., Xu J. (2016) AUC-Maximized Deep Convolutional Neural Fields for Protein Sequence Labeling. In: Frasconi P., Landwehr N., Manco G., Vreeken J. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2016. Lecture Notes in Computer Science, vol 9852. Springer, Cham


Deep Convolutional Neural Networks (DCNN) has shown excellent performance in a variety of machine learning tasks. This paper presents Deep Convolutional Neural Fields (DeepCNF), an integration of DCNN with Conditional Random Field (CRF), for sequence labeling with an imbalanced label distribution. The widely-used training methods, such as maximum-likelihood and maximum labelwise accuracy, do not work well on imbalanced data. To handle this, we present a new training algorithm called maximum-AUC for DeepCNF. That is, we train DeepCNF by directly maximizing the empirical Area Under the ROC Curve (AUC), which is an unbiased measurement for imbalanced data. To fulfill this, we formulate AUC in a pairwise ranking framework, approximate it by a polynomial function and then apply a gradient-based procedure to optimize it. Our experimental results confirm that maximum-AUC greatly outperforms the other two training methods on 8-state secondary structure prediction and disorder prediction since their label distributions are highly imbalanced and also has similar performance as the other two training methods on solvent accessibility prediction, which has three equally-distributed labels. Furthermore, our experimental results show that our AUC-trained DeepCNF models greatly outperform existing popular predictors of these three tasks. The data and software related to this paper are available at

Supplementary material

431508_1_En_1_MOESM1_ESM.pdf (647 kb)
Supplementary material 1 (pdf 647 KB)

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Toyota Technological Institute at ChicagoChicagoUSA
  2. 2.University of ChicagoChicagoUSA

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