1k citations · 1.8k across the 20 of their papers we have counts for
20 papers
Leveraging generative hallucination and biophysics-informed modeling for unified biomolecular sequence-structure co-design
Xuefeng Liu, Mingxuan Cao, Xiao Luo +5
Biomolecular design underpins applications from molecular recognition to therapeutics and synthetic biology, yet de novo interaction design remains challenging-especially for DNA/R…
SurfDesign: Effective Protein Design on Molecular Surfaces
Fang Wu, Shuting Jin, Xiangru Tang +5
Protein function is largely determined by molecular surface geometry and physicochemical complementarity, yet most protein design methods condition only on backbone structure. We i…
Integration of Pre-trained Protein Language Models into Geometric Deep Learning Networks
Fang Wu, Lirong Wu, Dragomir Radev +2
Geometric deep learning has recently achieved great success in non-Euclidean domains, and learning on 3D structures of large biomolecules is emerging as a distinct research area. H…
Distance-based Protein Folding Powered by Deep Learning
Jinbo Xu
Contact-assisted protein folding has made very good progress, but two challenges remain. One is accurate contact prediction for proteins lack of many sequence homologs and the othe…
Real-value and confidence prediction of protein backbone dihedral angles through a hybrid method of clustering and deep learning
Yujuan Gao, Sheng Wang, Minghua Deng +1
Background. Protein dihedral angles provide a detailed description of protein local conformation. Predicted dihedral angles can be used to narrow down the conformational space of t…
Folding membrane proteins by deep transfer learning
Sheng Wang, Zhen Li, Yizhou Yu +1
Computational elucidation of membrane protein (MP) structures is challenging partially due to lack of sufficient solved structures for homology modeling. Here we describe a high-th…
Predicting membrane protein contacts from non-membrane proteins by deep transfer learning
Zhen Li, Sheng Wang, Yizhou Yu +1
Computational prediction of membrane protein (MP) structures is very challenging partially due to lack of sufficient solved structures for homology modeling. Recently direct evolut…
Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model
Sheng Wang, Siqi Sun, Zhen Li +2
Recently exciting progress has been made on protein contact prediction, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very…
Joint alignment of multiple protein-protein interaction networks via convex optimization
Somaye Hashemifar, Qixing Huang, Jinbo XU
Motivation: High-throughput experimental techniques have been producing more and more protein-protein interaction (PPI) data. PPI network alignment greatly benefits the understandi…
Network Inference by Learned Node-Specific Degree Prior
Qingming Tang, Lifu Tu, Weiran Wang +1
We propose a novel method for network inference from partially observed edges using a node-specific degree prior. The degree prior is derived from observed edges in the network to…
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…
MRFalign: Protein Homology Detection through Alignment of Markov Random Fields
Jianzhu Ma, Sheng Wang, Zhiyong Wang +1
Sequence-based protein homology detection has been extensively studied and so far the most sensitive method is based upon comparison of protein sequence profiles, which are derived…
Protein Contact Prediction by Integrating Joint Evolutionary Coupling Analysis and Supervised Learning
Jianzhu Ma, Sheng Wang, Zhiyong Wang +1
Protein contacts contain important information for protein structure and functional study, but contact prediction from sequence remains very challenging. Both evolutionary coupling…
Predicting protein contact map using evolutionary and physical constraints by integer programming (extended version)
Zhiyong Wang, Jinbo Xu
Motivation. Protein contact map describes the pairwise spatial and functional relationship of residues in a protein and contains key information for protein 3D structure prediction…