101 citations · 463 across the 23 of their papers we have counts for
23 papers
DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design
Jiaqi Guan, Xiangxin Zhou, Yuwei Yang +6
Designing 3D ligands within a target binding site is a fundamental task in drug discovery. Existing structured-based drug design methods treat all ligand atoms equally, which ignor…
xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data
Jing Gong, Minsheng Hao, Xingyi Cheng +6
Advances in high-throughput sequencing technology have led to significant progress in measuring gene expressions at the single-cell level. The amount of publicly available single-c…
MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation
Xingang Peng, Jiaqi Guan, Qiang Liu +1
Deep generative models have recently achieved superior performance in 3D molecule generation. Most of them first generate atoms and then add chemical bonds based on the generated a…
3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction
Jiaqi Guan, Wesley Wei Qian, Xingang Peng +3
Rich data and powerful machine learning models allow us to design drugs for a specific protein target \textit{in silico}. Recently, the inclusion of 3D structures during targeted d…
Efficient Meta Reinforcement Learning for Preference-based Fast Adaptation
Zhizhou Ren, Anji Liu, Yitao Liang +2
Learning new task-specific skills from a few trials is a fundamental challenge for artificial intelligence. Meta reinforcement learning (meta-RL) tackles this problem by learning t…
Boosting the Cycle Counting Power of Graph Neural Networks with I-GNNs
Yinan Huang, Xingang Peng, Jianzhu Ma +1
Message Passing Neural Networks (MPNNs) are a widely used class of Graph Neural Networks (GNNs). The limited representational power of MPNNs inspires the study of provably powerful…
3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design
Yinan Huang, Xingang Peng, Jianzhu Ma +1
Deep learning has achieved tremendous success in designing novel chemical compounds with desirable pharmaceutical properties. In this work, we focus on a new type of drug design pr…
Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets
Xingang Peng, Shitong Luo, Jiaqi Guan +3
Deep generative models have achieved tremendous success in designing novel drug molecules in recent years. A new thread of works have shown the great potential in advancing the spe…
Equivariant Point Cloud Analysis via Learning Orientations for Message Passing
Shitong Luo, Jiahan Li, Jiaqi Guan +4
Equivariance has been a long-standing concern in various fields ranging from computer vision to physical modeling. Most previous methods struggle with generality, simplicity, and e…
Provable Constrained Stochastic Convex Optimization with XOR-Projected Gradient Descent
Fan Ding, Yijie Wang, Jianzhu Ma +1
Provably solving stochastic convex optimization problems with constraints is essential for various problems in science, business, and statistics. Recently proposed XOR-Stochastic G…
Device-system Co-design of Photonic Neuromorphic Processor using Reinforcement Learning
Yingheng Tang, Princess Tara Zamani, Ruiyang Chen +4
The incorporation of high-performance optoelectronic devices into photonic neuromorphic processors can substantially accelerate computationally intensive operations in machine lear…
Orientation-Aware Graph Neural Networks for Protein Structure Representation Learning
Jiahan Li, Shitong Luo, Congyue Deng +5
By folding into particular 3D structures, proteins play a key role in living beings. To learn meaningful representation from a protein structure for downstream tasks, not only the…
Contrastive Cycle Adversarial Autoencoders for Single-cell Multi-omics Alignment and Integration
Xuesong Wang, Zhihang Hu, Tingyang Yu +5
Muilti-modality data are ubiquitous in biology, especially that we have entered the multi-omics era, when we can measure the same biological object (cell) from different aspects (o…
Fast Projection onto the Capped Simplex with Applications to Sparse Regression in Bioinformatics
Andersen Ang, Jianzhu Ma, Nianjun Liu +2
We consider the problem of projecting a vector onto the so-called k-capped simplex, which is a hyper-cube cut by a hyperplane. For an n-dimensional input vector with bounded elemen…
Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns
Susheel Suresh, Vinith Budde, Jennifer Neville +2
Graph neural networks (GNNs) have achieved tremendous success on multiple graph-based learning tasks by fusing network structure and node features. Modern GNN models are built upon…
Neural Predicting Higher-order Patterns in Temporal Networks
Yunyu Liu, Jianzhu Ma, Pan Li
Dynamic systems that consist of a set of interacting elements can be abstracted as temporal networks. Recently, higher-order patterns that involve multiple interacting nodes have b…
Quantifying Spatial Homogeneity of Urban Road Networks via Graph Neural Networks
Jiawei Xue, Nan Jiang, Senwei Liang +4
Quantifying the topological similarities of different parts of urban road networks (URNs) enables us to understand the urban growth patterns. While conventional statistics provide…
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…
Protein Structure Prediction by Protein Alignments
Jianzhu Ma
Proteins are the basic building blocks of life. They usually perform functions by folding to a particular structure. Understanding the folding process could help the researchers to…
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…
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…