Author name cluster

Ben Cao

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

AAAI Conference 2026 Conference Paper

Learning Structurally Stabilized Representations for Lossless DNA Storage

Ben Cao
Xue Li
Tiantian He
Bin Wang
Shihua Zhou
Xiaohu Wu
Qiang Zhang

This paper presents Reed-Solomon coded single-stranded representation learning (RSRL), a novel end-to-end model for learning representations for lossless DNA data storage. In contrast to existing learning-based methods, RSRL is inspired by both error-correction codec and structural biology. Specifically, RSRL first learns the representations for the subsequent storage from the binary data transformed by the Reed-Solomon codec (RS code). Then, the representations are masked by an RS-code-informed mask to focus on correcting the burst errors occurring in the learning process. The synergy of RS masks and graph attention enables active error localization, breaking through the limitations of traditional passive error correction. With the decoded representations with error corrections, a novel biologically stabilized loss is formulated to regularize the data representations to possess stable single-stranded structures. By incorporating these novel strategies, RSRL can learn highly durable, dense, and lossless representations for subsequent storage tasks in DNA sequences. The proposed RSRL has been compared with a number of baselines in real-world tasks of multi-type data storage. The experimental results obtained demonstrate that RSRL can store diverse types of data with much higher information density and durability, but much lower error rates.

PDF Details DOI

JBHI Journal 2025 Journal Article

Predicting Mutation-Disease Associations Through Protein Interactions Via Deep Learning

Xue Li
Ben Cao
Jianmin Wang
Xiangyu Meng
Shuang Wang
Yu Huang
Enrico Petretto
Tao Song

Disease is one of the primary factors affecting life activities, with complex etiologies often influenced by gene expression and mutation. Currently, wet lab experiments have analyzed the mechanisms of mutations, but these are usually limited by the costs of wet experiments and constraints in sample types and scales. Therefore, this paper constructs a real-world mutation-induced disease dataset and proposes Capsule and Graph topology networks with Multi-head attention (CGM) to predict the mutation-disease associations. CGM can accurately predict protein mutation-disease associations, and to further elucidate the pathogenicity of protein mutations, we also verified that protein mutations lead to protein structural alterations by the model, which suggests that mutation-induced conformational changes may be an important pathogenic factor. Limited by the size of the mutated protein dataset, we also performed experiments on benchmark and imbalanced datasets, where CGM mined 22 unknown protein interaction pairs from the benchmark dataset, better illustrating the potential of CGM in predicting mutation-disease associations. In summary, this paper curates a real dataset. It proposes that CGM predicts protein mutations and disease associations, providing a novel tool for further understanding of biomolecular pathways and disease mechanisms.

Details DOI