Liwen Xu

AAAI Conference 2025 Conference Paper

Accurate Nucleic Acid-Binding Residue Identification Based Domain-Adaptive Protein Language Model and Explainable Geometric Deep Learning

• Wenwu Zeng
• Liangrui Pan
• Boya Ji
• Liwen Xu
• Shaoliang Peng

Protein-nucleic acid interactions play a fundamental and critical role in a wide range of life activities. Accurate identification of nucleic acid-binding residues helps to understand the intrinsic mechanisms of the interactions. However, the accuracy and interpretability of existing computational methods for recognizing nucleic acid-binding residues need to be further improved. Here, we propose a novel method called GeSite based the domain-adaptive protein language model and E(3)-equivariant graph neural network. Prediction results across multiple benchmark test sets demonstrate that GeSite is superior or comparable to state-of-the-art prediction methods. The MCC values of GeSite are 0.522 and 0.326 for the one DNA-binding residue test set and one RNA-binding resi-due test set, which are 0.57 and 38.14% higher than that of the second-best method, respectively. Detailed experi-mental results suggest that the advanced performance of GeSite lies in the well-designed nucleic acid-binding pro-tein adaptive language model. Additionally, interpretabil-ity analysis exposes the perception of the prediction mod-el on various remote and close functional domains, which is the source of its discernment ability.

EAAI Journal 2018 Journal Article

Online activity recognition and daily habit modeling for solitary elderly through indoor position-based stigmergy

• Zhichao Tan
• Liwen Xu
• Wei Zhong
• Xuemei Guo
• Guoli Wang

UAI Conference 2014 Conference Paper

Correlated Compressive Sensing for Networked Data

• Tianlin Shi
• Da Tang
• Liwen Xu
• Thomas Moscibroda

We consider the problem of recovering sparse correlated data on networks. To improve accuracy and reduce costs, it is strongly desirable to take the potentially useful side-information of network structure into consideration. In this paper we present a novel correlated compressive sensing method called CorrCS for networked data. By naturally extending Bayesian compressive sensing, we extract correlations from network topology and encode them into a graphical model as prior. Then we derive posterior inference algorithms for the recovery of jointly sparse and correlated networked data. First, we design algorithms to recover the data based on pairwise correlations between neighboring nodes in the network. Next, we generalize this model through a diffusion process to capture higher-order correlations. Both real-valued and binary data are considered. Our models are extensively tested on several real datasets from social and sensor networks and are shown to outperform baseline compressive sensing models in terms of recovery performance.