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Wenzheng Bao
Researcher at Xuzhou Institute of Technology
Publications - 61
Citations - 691
Wenzheng Bao is an academic researcher from Xuzhou Institute of Technology. The author has contributed to research in topics: Computer science & Biology. The author has an hindex of 10, co-authored 45 publications receiving 407 citations. Previous affiliations of Wenzheng Bao include China University of Mining and Technology & Tongji University.
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Journal ArticleDOI
Recurrent Neural Network for Predicting Transcription Factor Binding Sites
TL;DR: This study proposes a model, named KEGRU, to identify TF binding sites by combining Bidirectional Gated Recurrent Unit (GRU) network with k-mer embedding, and constructs a deep bidirectional GRU model for feature learning and classification.
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Novel human microbe-disease association prediction using network consistency projection.
TL;DR: NCPHMDA is a non-parametric universal network-based method which can simultaneously predict associated microbes for investigated diseases but does not require negative samples and is anticipated that NCPHMDA would become an effective biological resource for clinical experimental guidance.
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Weakly-Supervised Convolutional Neural Network Architecture for Predicting Protein-DNA Binding
TL;DR: A weakly-supervised convolutional neural network architecture (WSCNN), combining multiple-instance learning (MIL) with CNN, to further boost the performance of predicting protein-DNA binding.
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Mutli-Features Prediction of Protein Translational Modification Sites
Wenzheng Bao,Chang-An Yuan,Youhua Zhang,Kyungsook Han,Asoke K. Nandi,Barry Honig,De-Shuang Huang +6 more
TL;DR: A combination of physical, chemical, statistical, and biological properties of a protein have been ulitized as the features, and a novel framework is proposed to predict a protein's post translational modification sites and the experimental results show that the proposed method has the ability to improve the accuracy in this classification issue.
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A novel deep model with multi-loss and efficient training for person re-identification
TL;DR: This study provides a comprehensive overview of the advantages and limitations of the two widely-used CNN frameworks in the PReID community, and presents a hybrid model that combines the advantages of both identification and triplet models.