Shuwen Xiong

Chengdu University of Information Technology

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Yongqing Zhang

Shandong University of Traditional Chinese Medicine

Quan Zou

University of Electronic Science and Technology of China

Yuhang Liu

Zhejiang University of Technology

Maocheng Wang

Chengdu University of Information Technology

Zixuan Wang

Institute of Hydrobiology, Biology Centre, Academy of Sciences of the Czech Republic

Jiliu Zhou

Sichuan University

Hong Luo

Institute of High Energy Physics

Zixuan Wang

Guilin University of Technology

Zixuan Wang

University of Chinese Academy of Sciences

Zixuan Wang

State Key Laboratory of Chemical Engineering

Cooperative Institutions

Chengdu University of Information Technology

Chinese Academy of Sciences

Sichuan University

Wenzhou Medical University

University of Electronic Science and Technology of China

Nanjing Medical University

Harbin Institute of Technology

Northwestern Polytechnical University

University of Chinese Academy of Sciences

Capital Medical University

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HGTDG: An Interpretable Heterogeneous Graph Transformer Framework for Cancer Driver Gene Prediction

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2023)

Shuwen Xiong Zixuan Wang Junming Zhang Guiquan Zhu Yongqing Zhang

Accurately predicting cancer driver genes remains challenging due to the increasing size and complexity of cancer genomic data. In this study, HGTDG is proposed, a heterogeneous graph transformer framework for predicting cancer driver genes and exploring downstream tasks. The framework includes a heterogeneous graph construction module that constructs a gene-protein heterogeneous network based on KEGG pathways and the protein-protein interactions from the STRING database. In addition, the framework introduces a novel heterogeneous graph transformer module that uses multi-head attention mechanisms for gene node embedding. The transformer module can capture dedicated representations for genes and edges. Finally, the generated gene embeddings are fed into the classification module to classify genes into driver and non-driver genes. The experiment results show that HGTDG outperforms the state-of-the-art methods regarding the area under the receiver operating characteristic curves (AUROC) and the area under the precision-recall curves (AUPRC).

Graph Embedding

10.1109/bibm58861.2023.10385343

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Densely Convolutional Neural Network for Transcription Factor Binding Sites Prediction Using DNA Sequence and Histone Modification

Beichen Li Zixuan Wang Shuwen Xiong Yongqing Zhang

Transcription factor binding sites (TFBSs) prediction is crucial for decoding cis-regulation. However, current deep learning methods fail to simultaneously consider the multiscale features from DNA sequences and histone modifications in an efficient manner. To this end, we propose a novel D ensely C onvolutional N eural N etwork using DNA S equence and H istone Modification, dubbed as DCNN-SH, for TFBSs prediction. Our model adopts densely convolutional blocks to reuse multi-length motifs and multi-order dependencies of nucleotides. This unique design allows our model to consider the multi-scale features using smaller convolutional kernels compared to current methods. Our work is the first to apply densely network for TFBSs prediction. Extensive experiments over 300 ChIP-seq datasets demonstrate that our model significantly outperforms several state-of-the-art prediction methods in terms of accuracy, ROC-AUC and PR-AUC.

DNA binding site

Sequence (biology)

10.1109/icaibd55127.2022.9820177

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Citations (1)

A heterogeneous graph transformer framework for accurate cancer driver gene prediction and downstream analysis

Methods (2024)

Shuwen Xiong Junming Zhang Hong Luo Zhang Yong-qing Qing Xiao

Graph Embedding

10.1016/j.ymeth.2024.09.018

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Towards a better understanding of TF-DNA binding prediction from genomic features

Computers in Biology and Medicine (2022)

Zixuan Wang Meiqin Gong Yuhang Liu Shuwen Xiong Maocheng Wang

DNA binding site

Compendium

10.1016/j.compbiomed.2022.105993

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Citations (16)

Solvothermal-assisted preparation of PdRhTe nanowires as an efficient electrocatalyst for ethylene glycol oxidation

New Journal of Chemistry (2021)

Peiyan Bi Xiaoping Wu Shuwen Xiong Wei Hong

A new strategy for the fabrication of composition-tunable PdRhTe nanowires with high performance toward ethylene glycol electrooxidation has been demonstrated.

10.1039/d1nj02654b

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Multiple sequence alignment based on deep reinforcement learning with self-attention and positional encoding

Bioinformatics (2023)

Yuhang Liu Hao Yuan Qiang Zhang Zixuan Wang Shuwen Xiong

Abstract Motivation Multiple sequence alignment (MSA) is one of the hotspots of current research and is commonly used in sequence analysis scenarios. However, there is no lasting solution for MSA because it is a Nondeterministic Polynomially complete problem, and the existing methods still have room to improve the accuracy. Results We propose Deep reinforcement learning with Positional encoding and self-Attention for MSA, based on deep reinforcement learning, to enhance the accuracy of the alignment Specifically, inspired by the translation technique in natural language processing, we introduce self-attention and positional encoding to improve accuracy and reliability. Firstly, positional encoding encodes the position of the sequence to prevent the loss of nucleotide position information. Secondly, the self-attention model is used to extract the key features of the sequence. Then input the features into a multi-layer perceptron, which can calculate the insertion position of the gap according to the features. In addition, a novel reinforcement learning environment is designed to convert the classic progressive alignment into progressive column alignment, gradually generating each column’s sub-alignment. Finally, merge the sub-alignment into the complete alignment. Extensive experiments based on several datasets validate our method’s effectiveness for MSA, outperforming some state-of-the-art methods in terms of the Sum-of-pairs and Column scores. Availability and implementation The process is implemented in Python and available as open-source software from https://github.com/ZhangLab312/DPAMSA.

Python

Nondeterministic algorithm

10.1093/bioinformatics/btad636

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Citations (1)

HAMPLE: deciphering TF-DNA binding mechanism in different cellular environments by characterizing higher-order nucleotide dependency

Bioinformatics (2023)

Zixuan Wang Shuwen Xiong Yun Yu Jiliu Zhou Yongqing Zhang

Abstract Motivation Transcription factor (TF) binds to conservative DNA binding sites in different cellular environments and development stages by physical interaction with interdependent nucleotides. However, systematic computational characterization of the relationship between higher-order nucleotide dependency and TF-DNA binding mechanism in diverse cell types remains challenging. Results Here, we propose a novel multi-task learning framework HAMPLE to simultaneously predict TF binding sites (TFBS) in distinct cell types by characterizing higher-order nucleotide dependencies. Specifically, HAMPLE first represents a DNA sequence through three higher-order nucleotide dependencies, including k-mer encoding, DNA shape and histone modification. Then, HAMPLE uses the customized gate control and the channel attention convolutional architecture to further capture cell-type-specific and cell-type-shared DNA binding motifs and epigenomic languages. Finally, HAMPLE exploits the joint loss function to optimize the TFBS prediction for different cell types in an end-to-end manner. Extensive experimental results on seven datasets demonstrate that HAMPLE significantly outperforms the state-of-the-art approaches in terms of auROC. In addition, feature importance analysis illustrates that k-mer encoding, DNA shape, and histone modification have predictive power for TF-DNA binding in different cellular environments and are complementary to each other. Furthermore, ablation study, and interpretable analysis validate the effectiveness of the customized gate control and the channel attention convolutional architecture in characterizing higher-order nucleotide dependencies. Availability and implementation The source code is available at https://github.com/ZhangLab312/Hample.

Epigenomics

DNA binding site

10.1093/bioinformatics/btad299

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Citations (3)

Uncovering the Relationship between Tissue-Specific TF-DNA Binding and Chromatin Features through a Transformer-Based Model

Genes (2022)

Yongqing Zhang Yuhang Liu Zixuan Wang Maocheng Wang Shuwen Xiong

Chromatin features can reveal tissue-specific TF-DNA binding, which leads to a better understanding of many critical physiological processes. Accurately identifying TF-DNA bindings and constructing their relationships with chromatin features is a long-standing goal in the bioinformatic field. However, this has remained elusive due to the complex binding mechanisms and heterogeneity among inputs. Here, we have developed the GHTNet (General Hybrid Transformer Network), a transformer-based model to predict TF-DNA binding specificity. The GHTNet decodes the relationship between tissue-specific TF-DNA binding and chromatin features via a specific input scheme of alternative inputs and reveals important gene regions and tissue-specific motifs. Our experiments show that the GHTNet has excellent performance, achieving about a 5% absolute improvement over existing methods. The TF-DNA binding mechanism analysis shows that the importance of TF-DNA binding features varies across tissues. The best predictor is based on the DNA sequence, followed by epigenomics and shape. In addition, cross-species studies address the limited data, thus providing new ideas in this case. Moreover, the GHTNet is applied to interpret the relationship among TFs, chromatin features, and diseases associated with AD46 tissue. This paper demonstrates that the GHTNet is an accurate and robust framework for deciphering tissue-specific TF-DNA binding and interpreting non-coding regions.

Epigenomics

10.3390/genes13111952

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Citations (9)

Predicting cell type-specific effects of variants on TF-DNA binding by meta-learning

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2022)

Yongqing Zhang Yuhang Liu Zixuan Wang Maocheng Wang Shuwen Xiong

Interpreting the regulatory code of gene expression and further understanding the functionality of noncoding variants on transcriptional effect is a crucial challenge. However, this remains difficult due to the complex association between SNPs and chromatin state. Here, we develop a meta-learning-based framework, U-TransNet, that can accurately predict the TF-DNA binding based on multiple chromatin features. Motivated by ab initio, our proposed framework contain two steps. (i) Meta-learning strategy is applied to predict chromatin profiles from DNA sequence. (ii) DNA sequence and all of these predicted chromatin features are used to predict TF-DNA binding affinity. Experiments demonstrate that U-TransNet has excellent performance, achieving significant improvements over existing methods in predicting base-resolution TF-DNA binding signals, TF binding sites, and motifs. We also demonstrate that integrating the more extended TFBS flank regions is a potential path to better understanding gene transcription. In addition, U-TransNet is applied to infer the effects of variants on TF-DNA binding affinity via in silico mutagenesis, and further to identify cell type-specific functional variants via comparing different cells. To the best of the authors' knowledge, U-TransNet provides an efficient end-to-end computational framework for deciphering cis-regulator evolution.

10.1109/bibm55620.2022.9995698

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Local augmented graph neural network for multi-omics cancer prognosis prediction and analysis

Methods (2023)

Yongqing Zhang Shuwen Xiong Zixuan Wang Yuhang Liu Hong Luo

Autoencoder

10.1016/j.ymeth.2023.02.011

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Citations (10)