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Protein secondary structure prediction using deep convolutional neural fields
Sheng Wang, Jian Peng, Jianzhu Ma +1
Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, current…
Predicting diverse M-best protein contact maps
Siqi Sun, Jianzhu Ma, Sheng Wang +1
Protein contacts contain important information for protein structure and functional study, but contact prediction from sequence information remains very challenging. Recently evolu…
AUC-maximized Deep Convolutional Neural Fields for Sequence Labeling
Sheng Wang, Siqi Sun, Jinbo Xu
Deep Convolutional Neural Networks (DCNN) has shown excellent performance in a variety of machine learning tasks. This manuscript presents Deep Convolutional Neural Fields (DeepCNF…
Bermuda: Bidirectional de novo assembly of transcripts with new insights for handling uneven coverage
Qingming Tang, Sheng Wang, Jian Peng +2
Motivation: RNA-seq has made feasible the analysis of a whole set of expressed mRNAs. Mapping-based assembly of RNA-seq reads sometimes is infeasible due to lack of high-quality re…
iTreePack: Protein Complex Side-Chain Packing by Dual Decomposition
Jian Peng, Raghavendra Hosur, Bonnie Berger +1
Protein side-chain packing is a critical component in obtaining the 3D coordinates of a structure and drug discovery. Single-domain protein side-chain packing has been thoroughly s…
Learning Scale-Free Networks by Dynamic Node-Specific Degree Prior
Qingming Tang, Siqi Sun, Jinbo Xu
Learning the network structure underlying data is an important problem in machine learning. This paper introduces a novel prior to study the inference of scale-free networks, which…
Exact Hybrid Covariance Thresholding for Joint Graphical Lasso
Qingming Tang, Chao Yang, Jian Peng +1
This paper considers the problem of estimating multiple related Gaussian graphical models from a -dimensional dataset consisting of different classes. Our work is based upon the…