AUC-Maximized Deep Convolutional Neural Fields for Protein Sequence Labeling

By Sheng Wang, Siqi Sun, Jinbo Xu. 2016

23 citations recorded by the source corpus.

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 https://github.com/realbigws/DeepCNF_AUC.

Subfields: Artificial Intelligence, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jianzhu Ma
2. Mingfu Shao
3. Xin Gao
4. Yiming Ying
5. Ramzan Umarov
6. Le Song
7. Yu Li
8. Sheng Wang

In Molecular Biology

1. Sheng Wang
2. Jianzhu Ma
3. Xin Gao
4. Yu Li
5. Mingfu Shao
6. Jinbo Xu
7. Ramzan Umarov
8. Yiming Ying

Citations in the corpus

• AUC Maximization in the Era of Big Data and AI: A Survey (2022, background): “Last but not least, AUC maximization has been successfully investigated in a variety of applications [6, 9, 43, 61, 77, 150, 153, 166, 167, 176, 204, 208], e.”
• DeepBound: accurate identification of transcript boundaries via deep convolutional neural fields (2017, methodology): “In this work, we propose a novel algorithmic framework, DeepBound, to identify boundaries of expressed transcripts from reads alignment by using deep convolutional neural fields (DeepCNF) (Wang et al., 2015, 2016c).”
• RNA Secondary Structure Prediction By Learning Unrolled Algorithms (2020, background): “A hand-tuned weight is needed to balance them while using `2 or crossentropy losses, but F1 loss handles this issue automatically, which can be useful for a number of problems (Wang et al., 2016; Li et al., 2017).”