Yu, Z., Su, Y., Lu, Y., Yang, Y., Wang, F., Zhang, S., Chang, Y., Wong, K. C., & Li, X. (2023). Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA. Nature Communications.
@article{0Topological,
title = {Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA},
author = {Yu, Zhuohan and Su, Yanchi and Lu, Yifu and Yang, Yuning and Wang, Fuzhou and Zhang, Shixiong and Chang, Yi and Wong, Ka Chun and Li, Xiangtao},
journal = {Nature Communications},
doi = {10.1038/s41467-023-36134-7},
year = {2023}
}
Zhu, H., Yang, Y., Wang, Y., Wang, F., Huang, Y., Chang, Y., Wong, K.-chun, & Li, X. (2023). Dynamic characterization and interpretation for protein-RNA interactions across diverse cellular conditions using HDRNet. Nature Communications, 14(1), 6824.
@article{zhu2023dynamic,
title = {Dynamic characterization and interpretation for protein-RNA interactions across diverse cellular conditions using HDRNet},
author = {Zhu, Haoran and Yang, Yuning and Wang, Yunhe and Wang, Fuzhou and Huang, Yujian and Chang, Yi and Wong, Ka-chun and Li, Xiangtao},
journal = {Nature Communications},
volume = {14},
number = {1},
pages = {6824},
doi = {10.1038/s41467-023-42547-1},
year = {2023},
publisher = {Nature Publishing Group UK London}
}
RNA-binding proteins play crucial roles in the regulation of gene expression, and understanding the interactions between RNAs and RBPs in distinct cellular conditions forms the basis for comprehending the underlying RNA function. However, current computational methods pose challenges to the cross-prediction of RNA-protein binding events across diverse cell lines and tissue contexts. Here, we develop HDRNet, an end-to-end deep learning-based framework to precisely predict dynamic RBP binding events under diverse cellular conditions. Our results demonstrate that HDRNet can accurately and efficiently identify binding sites, particularly for dynamic prediction, outperforming other state-of-the-art models on 261 linear RNA datasets from both eCLIP and CLIP-seq, supplemented with additional tissue data. Moreover, we conduct motif and interpretation analyses to provide fresh insights into the pathological mechanisms underlying RNA-RBP interactions from various perspectives. Our functional genomic analysis further explores the gene-human disease associations, uncovering previously uncharacterized observations for a broad range of genetic disorders.
Fan, Y., Wang, Y., Wang, F., Huang, L., Yang, Y., Wong, K.-C., & Li, X. (2023). Reliable Identification and Interpretation of Single-Cell Molecular Heterogeneity and Transcriptional Regulation using Dynamic Ensemble Pruning. Advanced Science, 10(22), 2205442.
@article{fan2023reliable,
title = {Reliable Identification and Interpretation of Single-Cell Molecular Heterogeneity and Transcriptional Regulation using Dynamic Ensemble Pruning},
author = {Fan, Yi and Wang, Yunhe and Wang, Fuzhou and Huang, Lei and Yang, Yuning and Wong, Ka-Chun and Li, Xiangtao},
journal = {Advanced Science},
volume = {10},
number = {22},
pages = {2205442},
year = {2023},
publisher = {Wiley Online Library},
doi = {10.1002/advs.202205442}
}
Zheng, Z., Chen, J., Chen, X., Huang, L., Xie, W., Lin, Q., Li, X., & Wong, K.-C. (2023). Enabling Single-Cell Drug Response Annotations from Bulk RNA-Seq Using SCAD. Advanced Science, 2204113.
@article{zheng2023enabling,
title = {Enabling Single-Cell Drug Response Annotations from Bulk RNA-Seq Using SCAD},
author = {Zheng, Zetian and Chen, Junyi and Chen, Xingjian and Huang, Lei and Xie, Weidun and Lin, Qiuzhen and Li, Xiangtao and Wong, Ka-Chun},
journal = {Advanced Science},
pages = {2204113},
year = {2023},
publisher = {Wiley Online Library},
doi = {10.1002/advs.202204113}
}
The single‐cell RNA sequencing (scRNA‐seq) quantifies the gene expression of individual cells, while the bulk RNA sequencing (bulk RNA‐seq) characterizes the mixed transcriptome of cells. The inference of drug sensitivities for individual cells can provide new insights to understand the mechanism of anti‐cancer response heterogeneity and drug resistance at the cellular resolution. However, pharmacogenomic information related to their corresponding scRNA‐Seq is often limited. Therefore, a transfer learning model is proposed to infer the drug sensitivities at single‐cell level. This framework learns bulk transcriptome profiles and pharmacogenomics information from population cell lines in a large public dataset and transfers the knowledge to infer drug efficacy of individual cells. The results suggest that it is suitable to learn knowledge from pre‐clinical cell lines to infer pre‐existing cell subpopulations with different drug sensitivities prior to drug exposure. In addition, the model offers a new perspective on drug combinations. It is observed that drug‐resistant subpopulation can be sensitive to other drugs (e.g., a subset of JHU006 is Vorinostat‐resistant while Gefitinib‐sensitive); such finding corroborates the previously reported drug combination (Gefitinib + Vorinostat) strategy in several cancer types. The identified drug sensitivity biomarkers reveal insights into the tumor heterogeneity and treatment at cellular resolution.
Wang, F., Alinejad-Rokny, H., Lin, J., Gao, T., Chen, X., Zheng, Z., Meng, L., Li, X., & Wong, K.-C. (2023). A Lightweight Framework For Chromatin Loop Detection at the Single-Cell Level. Advanced Science, 2303502.
@article{wang2023lightweight,
title = {A Lightweight Framework For Chromatin Loop Detection at the Single-Cell Level},
author = {Wang, Fuzhou and Alinejad-Rokny, Hamid and Lin, Jiecong and Gao, Tingxiao and Chen, Xingjian and Zheng, Zetian and Meng, Lingkuan and Li, Xiangtao and Wong, Ka-Chun},
journal = {Advanced Science},
pages = {2303502},
year = {2023},
publisher = {Wiley Online Library},
doi = {10.1002/advs.202303502}
}
Single-cell Hi-C (scHi-C) has made it possible to analyze chromatin organization at the single-cell level. However, scHi-C experiments generate inherently sparse data, which poses a challenge for loop calling methods. The existing approach performs significance tests across the imputed dense contact maps, leading to substantial computational overhead and loss of information at the single-cell level. To overcome this limitation, a lightweight framework called scGSLoop is proposed, which sets a new paradigm for scHi-C loop calling by adapting the training and inferencing strategies of graph-based deep learning to leverage the sequence features and 1D positional information of genomic loci. With this framework, sparsity is no longer a challenge, but rather an advantage that the model leverages to achieve unprecedented computational efficiency. Compared to existing methods, scGSLoop makes more accurate predictions and is able to identify more loops that have the potential to play regulatory roles in genome functioning. Moreover, scGSLoop preserves single-cell information by identifying a distinct group of loops for each individual cell, which not only enables an understanding of the variability of chromatin looping states between cells, but also allows scGSLoop to be extended for the investigation of multi-connected hubs and their underlying mechanisms.
Wang, Y., Yu, Z., Li, S., Bian, C., Liang, Y., Wong, K.-C., & Li, X. (2023). scBGEDA: deep single-cell clustering analysis via a dual denoising autoencoder with bipartite graph ensemble clustering. Bioinformatics, 39(2), btad075.
@article{wang2023scbgeda,
title = {scBGEDA: deep single-cell clustering analysis via a dual denoising autoencoder with bipartite graph ensemble clustering},
author = {Wang, Yunhe and Yu, Zhuohan and Li, Shaochuan and Bian, Chuang and Liang, Yanchun and Wong, Ka-Chun and Li, Xiangtao},
journal = {Bioinformatics},
volume = {39},
number = {2},
pages = {btad075},
year = {2023},
publisher = {Oxford University Press},
doi = {10.1093/bioinformatics/btad075}
}
Motivation
Single-cell RNA sequencing (scRNA-seq) is an increasingly popular technique for transcriptomic analysis of gene expression at the single-cell level. Cell-type clustering is the first crucial task in the analysis of scRNA-seq data that facilitates accurate identification of cell types and the study of the characteristics of their transcripts. Recently, several computational models based on a deep autoencoder and the ensemble clustering have been developed to analyze scRNA-seq data. However, current deep autoencoders are not sufficient to learn the latent representations of scRNA-seq data, and obtaining consensus partitions from these feature representations remains under-explored.
Results
To address this challenge, we propose a single-cell deep clustering model via a dual denoising autoencoder with bipartite graph ensemble clustering called scBGEDA, to identify specific cell populations in single-cell transcriptome profiles. First, a single-cell dual denoising autoencoder network is proposed to project the data into a compressed low-dimensional space and that can learn feature representation via explicit modeling of synergistic optimization of the zero-inflated negative binomial reconstruction loss and denoising reconstruction loss. Then, a bipartite graph ensemble clustering algorithm is designed to exploit the relationships between cells and the learned latent embedded space by means of a graph-based consensus function. Multiple comparison experiments were conducted on 20 scRNA-seq datasets from different sequencing platforms using a variety of clustering metrics. The experimental results indicated that scBGEDA outperforms other state-of-the-art methods on these datasets, and also demonstrated its scalability to large-scale scRNA-seq datasets. Moreover, scBGEDA was able to identify cell-type specific marker genes and provide functional genomic analysis by quantifying the influence of genes on cell clusters, bringing new insights into identifying cell types and characterizing the scRNA-seq data from different perspectives.
Availability and implementation
The source code of scBGEDA is available at https://github.com/wangyh082/scBGEDA. The software and the supporting data can be downloaded from https://figshare.com/articles/software/scBGEDA/19657911.
Supplementary information
Supplementary data are available at Bioinformatics online.
Sun, P., Fan, S., Li, S., Zhao, Y., Lu, C., Wong, K.-C., & Li, X. (2023). Automated exploitation of deep learning for cancer patient stratification across multiple types. Bioinformatics, 39(11), btad654.
@article{sun2023automated,
title = {Automated exploitation of deep learning for cancer patient stratification across multiple types},
author = {Sun, Pingping and Fan, Shijie and Li, Shaochuan and Zhao, Yingwei and Lu, Chang and Wong, Ka-Chun and Li, Xiangtao},
journal = {Bioinformatics},
volume = {39},
number = {11},
pages = {btad654},
year = {2023},
publisher = {Oxford University Press},
doi = {10.1093/bioinformatics/btad654}
}
Motivation
Recent frameworks based on deep learning have been developed to identify cancer subtypes from high-throughput gene expression profiles. Unfortunately, the performance of deep learning is highly dependent on its neural network architectures which are often hand-crafted with expertise in deep neural networks, meanwhile, the optimization and adjustment of the network are usually costly and time consuming.
Results
To address such limitations, we proposed a fully automated deep neural architecture search model for diagnosing consensus molecular subtypes from gene expression data (DNAS). The proposed model uses ant colony algorithm, one of the heuristic swarm intelligence algorithms, to search and optimize neural network architecture, and it can automatically find the optimal deep learning model architecture for cancer diagnosis in its search space. We validated DNAS on eight colorectal cancer datasets, achieving the average accuracy of 95.48%, the average specificity of 98.07%, and the average sensitivity of 96.24%, respectively. Without the loss of generality, we investigated the general applicability of DNAS further on other cancer types from different platforms including lung cancer and breast cancer, and DNAS achieved an area under the curve of 95% and 96%, respectively. In addition, we conducted gene ontology enrichment and pathological analysis to reveal interesting insights into cancer subtype identification and characterization across multiple cancer types.
Availability and implementation
The source code and data can be downloaded from https://github.com/userd113/DNAS-main. And the web server of DNAS is publicly accessible at 119.45.145.120:5001.
Su, Y., Wang, F., Zhang, S., Liang, Y., Wong, K.-C., & Li, X. (2022). scWMC: weighted matrix completion-based imputation of scRNA-seq data via prior subspace information. Bioinformatics, 38(19), 4537–4545.
@article{su2022scwmc,
title = {scWMC: weighted matrix completion-based imputation of scRNA-seq data via prior subspace information},
author = {Su, Yanchi and Wang, Fuzhou and Zhang, Shixiong and Liang, Yanchun and Wong, Ka-Chun and Li, Xiangtao},
journal = {Bioinformatics},
volume = {38},
number = {19},
pages = {4537--4545},
year = {2022},
publisher = {Oxford University Press},
doi = {10.1093/bioinformatics/btac570}
}
Motivation
Single-cell RNA sequencing (scRNA-seq) can provide insight into gene expression patterns at the resolution of individual cells, which offers new opportunities to study the behavior of different cell types. However, it is often plagued by dropout events, a phenomenon where the expression value of a gene tends to be measured as zero in the expression matrix due to various technical defects.
Results
In this article, we argue that borrowing gene and cell information across column and row subspaces directly results in suboptimal solutions due to the noise contamination in imputing dropout values. Thus, to impute more precisely the dropout events in scRNA-seq data, we develop a regularization for leveraging that imperfect prior information to estimate the true underlying prior subspace and then embed it in a typical low-rank matrix completion-based framework, named scWMC. To evaluate the performance of the proposed method, we conduct comprehensive experiments on simulated and real scRNA-seq data. Extensive data analysis, including simulated analysis, cell clustering, differential expression analysis, functional genomic analysis, cell trajectory inference and scalability analysis, demonstrate that our method produces improved imputation results compared to competing methods that benefits subsequent downstream analysis.
Availability and implementation
The source code is available at https://github.com/XuYuanchi/scWMC and test data is available at https://doi.org/10.5281/zenodo.6832477.
Supplementary information
Supplementary data are available at Bioinformatics online.
Lu, Y., Yu, Z., Wang, Y., Ma, Z., Wong, K.-C., & Li, X. (2022). GMHCC: high-throughput analysis of biomolecular data using graph-based multiple hierarchical consensus clustering. Bioinformatics, 38(11), 3020–3028. https://doi.org/10.1093/bioinformatics/btac290
@article{Lu2022GMHCC,
author = {Lu, Yifu and Yu, Zhuohan and Wang, Yunhe and Ma, Zhiqiang and Wong, Ka-Chun and Li, Xiangtao},
title = {GMHCC: high-throughput analysis of biomolecular data using graph-based multiple hierarchical consensus clustering},
journal = {Bioinformatics},
volume = {38},
number = {11},
pages = {3020-3028},
year = {2022},
month = apr,
issn = {1367-4803},
doi = {10.1093/bioinformatics/btac290},
url = {https://doi.org/10.1093/bioinformatics/btac290}
}
Thanks to the development of high-throughput sequencing technologies, massive amounts of various biomolecular data have been accumulated to revolutionize the study of genomics and molecular biology. One of the main challenges in analyzing this biomolecular data is to cluster their subtypes into subpopulations to facilitate subsequent downstream analysis. Recently, many clustering methods have been developed to address the biomolecular data. However, the computational methods often suffer from many limitations such as high dimensionality, data heterogeneity and noise.In our study, we develop a novel Graph-based Multiple Hierarchical Consensus Clustering (GMHCC) method with an unsupervised graph-based feature ranking (FR) and a graph-based linking method to explore the multiple hierarchical information of the underlying partitions of the consensus clustering for multiple types of biomolecular data. Indeed, we first propose to use a graph-based unsupervised FR model to measure each feature by building a graph over pairwise features and then providing each feature with a rank. Subsequently, to maintain the diversity and robustness of basic partitions (BPs), we propose multiple diverse feature subsets to generate several BPs and then explore the hierarchical structures of the multiple BPs by refining the global consensus function. Finally, we develop a new graph-based linking method, which explicitly considers the relationships between clusters to generate the final partition. Experiments on multiple types of biomolecular data including 35 cancer gene expression datasets and eight single-cell RNA-seq datasets validate the effectiveness of our method over several state-of-the-art consensus clustering approaches. Furthermore, differential gene analysis, gene ontology enrichment analysis and KEGG pathway analysis are conducted, providing novel insights into cell developmental lineages and characterization mechanisms.The source code is available at GitHub: https://github.com/yifuLu/GMHCC. The software and the supporting data can be downloaded from: https://figshare.com/articles/software/GMHCC/17111291.Supplementary data are available at Bioinformatics online.
Wang, Y., Yang, Y., Ma, Z., Wong, K.-C., & Li, X. (2021). EDCNN: identification of genome-wide RNA-binding proteins using evolutionary deep convolutional neural network. Bioinformatics, 38(3), 678–686. https://doi.org/10.1093/bioinformatics/btab739
@article{Wang2021EDCNN,
author = {Wang, Yawei and Yang, Yuning and Ma, Zhiqiang and Wong, Ka-Chun and Li, Xiangtao},
title = {EDCNN: identification of genome-wide RNA-binding proteins using evolutionary deep convolutional neural network},
journal = {Bioinformatics},
volume = {38},
number = {3},
pages = {678-686},
year = {2021},
month = oct,
issn = {1367-4803},
doi = {10.1093/bioinformatics/btab739},
url = {https://doi.org/10.1093/bioinformatics/btab739},
eprint = {https://academic.oup.com/bioinformatics/article-pdf/38/3/678/49007219/btab739.pdf}
}
RNA-binding proteins (RBPs) are a group of proteins associated with RNA regulation and metabolism, and play an essential role in mediating the maturation, transport, localization and translation of RNA. Recently, Genome-wide RNA-binding event detection methods have been developed to predict RBPs. Unfortunately, the existing computational methods usually suffer some limitations, such as high-dimensionality, data sparsity and low model performance.Deep convolution neural network has a useful advantage for solving high-dimensional and sparse data. To improve further the performance of deep convolution neural network, we propose evolutionary deep convolutional neural network (EDCNN) to identify protein–RNA interactions by synergizing evolutionary optimization with gradient descent to enhance deep conventional neural network. In particular, EDCNN combines evolutionary algorithms and different gradient descent models in a complementary algorithm, where the gradient descent and evolution steps can alternately optimize the RNA-binding event search. To validate the performance of EDCNN, an experiment is conducted on two large-scale CLIP-seq datasets, and results reveal that EDCNN provides superior performance to other state-of-the-art methods. Furthermore, time complexity analysis, parameter analysis and motif analysis are conducted to demonstrate the effectiveness of our proposed algorithm from several perspectives.The EDCNN algorithm is available at GitHub: https://github.com/yaweiwang1232/EDCNN. Both the software and the supporting data can be downloaded from: https://figshare.com/articles/software/EDCNN/16803217.Supplementary data are available at Bioinformatics online.
@article{Li2018Singlecell,
author = {Li, Xiangtao and Zhang, Shixiong and Wong, Ka-Chun},
title = {Single-cell RNA-seq interpretations using evolutionary multiobjective ensemble pruning},
journal = {Bioinformatics},
volume = {35},
number = {16},
pages = {2809-2817},
year = {2018},
month = dec,
issn = {1367-4803},
doi = {10.1093/bioinformatics/bty1056},
url = {https://doi.org/10.1093/bioinformatics/bty1056},
eprint = {https://academic.oup.com/bioinformatics/article-pdf/35/16/2809/50719264/bty1056.pdf}
}
In recent years, single-cell RNA sequencing enables us to discover cell types or even subtypes. Its increasing availability provides opportunities to identify cell populations from single-cell RNA-seq data. Computational methods have been employed to reveal the gene expression variations among multiple cell populations. Unfortunately, the existing ones can suffer from realistic restrictions such as experimental noises, numerical instability, high dimensionality and computational scalability.We propose an evolutionary multiobjective ensemble pruning algorithm (EMEP) that addresses those realistic restrictions. Our EMEP algorithm first applies the unsupervised dimensionality reduction to project data from the original high dimensions to low-dimensional subspaces; basic clustering algorithms are applied in those new subspaces to generate different clustering results to form cluster ensembles. However, most of those cluster ensembles are unnecessarily bulky with the expense of extra time costs and memory consumption. To overcome that problem, EMEP is designed to dynamically select the suitable clustering results from the ensembles. Moreover, to guide the multiobjective ensemble evolution, three cluster validity indices including the overall cluster deviation, the within-cluster compactness and the number of basic partition clusters are formulated as the objective functions to unleash its cell type discovery performance using evolutionary multiobjective optimization. We applied EMEP to 55 simulated datasets and seven real single-cell RNA-seq datasets, including six single-cell RNA-seq dataset and one large-scale dataset with 3005 cells and 4412 genes. Two case studies are also conducted to reveal mechanistic insights into the biological relevance of EMEP. We found that EMEP can achieve superior performance over the other clustering algorithms, demonstrating that EMEP can identify cell populations clearly.EMEP is written in Matlab and available at https://github.com/lixt314/EMEPSupplementary data are available at Bioinformatics online.
Wang, Y., Bian, C., Wong, K.-C., Li, X., & Yang, S. (2022). Multiobjective Deep Clustering and its Applications in Single-cell RNA-seq Data. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(8), 5016–5027. http://dx.doi.org/10.1109/tsmc.2021.3112049
@article{Wang_2022,
title = {Multiobjective Deep Clustering and its Applications in Single-cell RNA-seq Data},
volume = {52},
issn = {2168-2232},
url = {http://dx.doi.org/10.1109/tsmc.2021.3112049},
doi = {10.1109/tsmc.2021.3112049},
number = {8},
journal = {IEEE Transactions on Systems, Man, and Cybernetics: Systems},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
author = {Wang, Yunhe and Bian, Chuang and Wong, Ka-Chun and Li, Xiangtao and Yang, Shengxiang},
year = {2022},
month = aug,
pages = {5016–5027}
}
Su, Y., Zhu, H., Wong, K.-C., Chang, Y., & Li, X. (2023). Hyperspectral Image Denoising via Weighted Multidirectional Low-Rank Tensor Recovery. IEEE Transactions on Cybernetics, 53(5), 2753–2766. http://dx.doi.org/10.1109/tcyb.2022.3208095
@article{Su_2023,
title = {Hyperspectral Image Denoising via Weighted Multidirectional Low-Rank Tensor Recovery},
volume = {53},
issn = {2168-2275},
url = {http://dx.doi.org/10.1109/tcyb.2022.3208095},
doi = {10.1109/tcyb.2022.3208095},
number = {5},
journal = {IEEE Transactions on Cybernetics},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
author = {Su, Yanchi and Zhu, Haoran and Wong, Ka-Chun and Chang, Yi and Li, Xiangtao},
year = {2023},
month = may,
pages = {2753–2766}
}
Wang, Y., Li, X., Wong, K.-C., Chang, Y., & Yang, S. (2022). Evolutionary Multiobjective Clustering Algorithms With Ensemble for Patient Stratification. IEEE Transactions on Cybernetics, 52(10), 11027–11040. http://dx.doi.org/10.1109/tcyb.2021.3069434
@article{Wang_2023,
title = {Evolutionary Multiobjective Clustering Algorithms With Ensemble for Patient Stratification},
volume = {52},
issn = {2168-2275},
url = {http://dx.doi.org/10.1109/tcyb.2021.3069434},
doi = {10.1109/tcyb.2021.3069434},
number = {10},
journal = {IEEE Transactions on Cybernetics},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
author = {Wang, Yunhe and Li, Xiangtao and Wong, Ka-Chun and Chang, Yi and Yang, Shengxiang},
year = {2022},
month = oct,
pages = {11027–11040}
}
@article{Li_2021,
title = {Multiobjective Genome-Wide RNA-Binding Event Identification From CLIP-Seq Data},
volume = {51},
issn = {2168-2275},
url = {http://dx.doi.org/10.1109/tcyb.2019.2960515},
doi = {10.1109/tcyb.2019.2960515},
number = {12},
journal = {IEEE Transactions on Cybernetics},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
author = {Li, Xiangtao and Zhang, Shixiong and Wong, Ka-Chun},
year = {2021},
month = dec,
pages = {5811–5824}
}
Li, X., & Wong, K.-C. (2019). Evolutionary Multiobjective Clustering and Its Applications to Patient Stratification. IEEE Transactions on Cybernetics, 49(5), 1680–1693. http://dx.doi.org/10.1109/tcyb.2018.2817480
@article{Li_2019,
title = {Evolutionary Multiobjective Clustering and Its Applications to Patient Stratification},
volume = {49},
issn = {2168-2275},
url = {http://dx.doi.org/10.1109/tcyb.2018.2817480},
doi = {10.1109/tcyb.2018.2817480},
number = {5},
journal = {IEEE Transactions on Cybernetics},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
author = {Li, Xiangtao and Wong, Ka-Chun},
year = {2019},
month = may,
pages = {1680–1693}
}
Wang, Y., Hou, Z., Yang, Y., Wong, K.-chun, & Li, X. (2022). Genome-wide identification and characterization of DNA enhancers with a stacked multivariate fusion framework. PLOS Computational Biology, 18(12), e1010779. http://dx.doi.org/10.1371/journal.pcbi.1010779
@article{Wang_2024,
title = {Genome-wide identification and characterization of DNA enhancers with a stacked multivariate fusion framework},
volume = {18},
issn = {1553-7358},
url = {http://dx.doi.org/10.1371/journal.pcbi.1010779},
doi = {10.1371/journal.pcbi.1010779},
number = {12},
journal = {PLOS Computational Biology},
publisher = {Public Library of Science (PLoS)},
author = {Wang, Yansong and Hou, Zilong and Yang, Yuning and Wong, Ka-chun and Li, Xiangtao},
editor = {Sinha, Saurabh},
year = {2022},
month = dec,
pages = {e1010779}
}
Cheng, Y., Su, Y., Yu, Z., Liang, Y., Wong, K.-C., & Li, X. (2023). Unsupervised Deep Embedded Fusion Representation of Single-Cell Transcriptomics. Proceedings of the AAAI Conference on Artificial Intelligence, 37(4), 5036–5044. http://dx.doi.org/10.1609/aaai.v37i4.25631
@article{Cheng_2023,
title = {Unsupervised Deep Embedded Fusion Representation of Single-Cell Transcriptomics},
volume = {37},
issn = {2159-5399},
url = {http://dx.doi.org/10.1609/aaai.v37i4.25631},
doi = {10.1609/aaai.v37i4.25631},
number = {4},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
publisher = {Association for the Advancement of Artificial Intelligence (AAAI)},
author = {Cheng, Yue and Su, Yanchi and Yu, Zhuohan and Liang, Yanchun and Wong, Ka-Chun and Li, Xiangtao},
year = {2023},
month = jun,
pages = {5036–5044}
}
Yu, Z., Lu, Y., Wang, Y., Tang, F., Wong, K.-C., & Li, X. (2022). ZINB-Based Graph Embedding Autoencoder for Single-Cell RNA-Seq Interpretations. Proceedings of the AAAI Conference on Artificial Intelligence, 36(4), 4671–4679. http://dx.doi.org/10.1609/aaai.v36i4.20392
@article{Yu_2022,
title = {ZINB-Based Graph Embedding Autoencoder for Single-Cell RNA-Seq Interpretations},
volume = {36},
issn = {2159-5399},
url = {http://dx.doi.org/10.1609/aaai.v36i4.20392},
doi = {10.1609/aaai.v36i4.20392},
number = {4},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
publisher = {Association for the Advancement of Artificial Intelligence (AAAI)},
author = {Yu, Zhuohan and Lu, Yifu and Wang, Yunhe and Tang, Fan and Wong, Ka-Chun and Li, Xiangtao},
year = {2022},
month = jun,
pages = {4671–4679}
}
Hou, Z., Yang, Y., Ma, Z., Wong, K.-C., & Li, X. (2022). EDLMPPI: Learning the Protein Language of Proteome-wide Protein-protein Binding Sites via Explainable Ensemble Deep Learning. Communications Biology. http://dx.doi.org/10.21203/rs.3.rs-1775089/v1
@article{Li_2022,
title = {EDLMPPI: Learning the Protein Language of Proteome-wide Protein-protein Binding Sites via Explainable Ensemble Deep Learning},
url = {http://dx.doi.org/10.21203/rs.3.rs-1775089/v1},
doi = {10.1038/s42003-023-04462-5},
publisher = {Research Square Platform LLC},
author = {Hou, Zilong and Yang, Yuning and Ma, Zhiqiang and Wong, Ka-Chun and Li, Xiangtao},
year = {2022},
month = jul,
journal = {Communications Biology}
}
@article{Wang_2025,
title = {GILoop: Robust chromatin loop calling across multiple sequencing depths on Hi-C data},
volume = {25},
issn = {2589-0042},
url = {http://dx.doi.org/10.1016/j.isci.2022.105535},
doi = {10.1016/j.isci.2022.105535},
number = {12},
journal = {iScience},
publisher = {Elsevier BV},
author = {Wang, Fuzhou and Gao, Tingxiao and Lin, Jiecong and Zheng, Zetian and Huang, Lei and Toseef, Muhammad and Li, Xiangtao and Wong, Ka-Chun},
year = {2022},
month = dec,
pages = {105535}
}
Wang, Y., Wong, K.-C., & Li, X. (2022). Exploring high-throughput biomolecular data with multiobjective robust continuous clustering. Information Sciences, 583, 239–265. http://dx.doi.org/10.1016/j.ins.2021.11.030
@article{Wang_2026,
title = {Exploring high-throughput biomolecular data with multiobjective robust continuous clustering},
volume = {583},
issn = {0020-0255},
url = {http://dx.doi.org/10.1016/j.ins.2021.11.030},
doi = {10.1016/j.ins.2021.11.030},
journal = {Information Sciences},
publisher = {Elsevier BV},
author = {Wang, Yunhe and Wong, Ka-Chun and Li, Xiangtao},
year = {2022},
month = jan,
pages = {239–265}
}
Toseef, M., Olayemi Petinrin, O., Wang, F., Rahaman, S., Liu, Z., Li, X., & Wong, K.-C. (2023). Deep transfer learning for clinical decision-making based on high-throughput data: comprehensive survey with benchmark results. Briefings in Bioinformatics, 24(4). http://dx.doi.org/10.1093/bib/bbad254
@article{Toseef_2023,
title = {Deep transfer learning for clinical decision-making based on high-throughput data: comprehensive survey with benchmark results},
volume = {24},
issn = {1477-4054},
url = {http://dx.doi.org/10.1093/bib/bbad254},
doi = {10.1093/bib/bbad254},
number = {4},
journal = {Briefings in Bioinformatics},
publisher = {Oxford University Press (OUP)},
author = {Toseef, Muhammad and Olayemi Petinrin, Olutomilayo and Wang, Fuzhou and Rahaman, Saifur and Liu, Zhe and Li, Xiangtao and Wong, Ka-Chun},
year = {2023},
month = jul
}
Huang, L., Lin, J., Liu, R., Zheng, Z., Meng, L., Chen, X., Li, X., & Wong, K.-C. (2022). CoaDTI: multi-modal co-attention based framework for drug–target interaction annotation. Briefings in Bioinformatics, 23(6). http://dx.doi.org/10.1093/bib/bbac446
@article{Huang_2022,
title = {CoaDTI: multi-modal co-attention based framework for drug–target interaction annotation},
volume = {23},
issn = {1477-4054},
url = {http://dx.doi.org/10.1093/bib/bbac446},
doi = {10.1093/bib/bbac446},
number = {6},
journal = {Briefings in Bioinformatics},
publisher = {Oxford University Press (OUP)},
author = {Huang, Lei and Lin, Jiecong and Liu, Rui and Zheng, Zetian and Meng, Lingkuan and Chen, Xingjian and Li, Xiangtao and Wong, Ka-Chun},
year = {2022},
month = oct
}
Toseef, M., Li, X., & Wong, K.-C. (2022). Reducing healthcare disparities using multiple multiethnic data distributions with fine-tuning of transfer learning. Briefings in Bioinformatics, 23(3). http://dx.doi.org/10.1093/bib/bbac078
@article{Toseef_2022,
title = {Reducing healthcare disparities using multiple multiethnic data distributions with fine-tuning of transfer learning},
volume = {23},
issn = {1477-4054},
url = {http://dx.doi.org/10.1093/bib/bbac078},
doi = {10.1093/bib/bbac078},
number = {3},
journal = {Briefings in Bioinformatics},
publisher = {Oxford University Press (OUP)},
author = {Toseef, Muhammad and Li, Xiangtao and Wong, Ka-Chun},
year = {2022},
month = mar
}
Yang, Y., Hou, Z., Wang, Y., Ma, H., Sun, P., Ma, Z., Wong, K.-C., & Li, X. (2022). HCRNet: high-throughput circRNA-binding event identification from CLIP-seq data using deep temporal convolutional network. Briefings in Bioinformatics, 23(2). http://dx.doi.org/10.1093/bib/bbac027
@article{Yang_2022,
title = {HCRNet: high-throughput circRNA-binding event identification from CLIP-seq data using deep temporal convolutional network},
volume = {23},
issn = {1477-4054},
url = {http://dx.doi.org/10.1093/bib/bbac027},
doi = {10.1093/bib/bbac027},
number = {2},
journal = {Briefings in Bioinformatics},
publisher = {Oxford University Press (OUP)},
author = {Yang, Yuning and Hou, Zilong and Wang, Yansong and Ma, Hongli and Sun, Pingping and Ma, Zhiqiang and Wong, Ka-Chun and Li, Xiangtao},
year = {2022},
month = feb
}
Huang, L., Lin, J., Li, X., Song, L., Zheng, Z., & Wong, K.-C. (2021). EGFI: drug–drug interaction extraction and generation with fusion of enriched entity and sentence information. Briefings in Bioinformatics, 23(1). http://dx.doi.org/10.1093/bib/bbab451
@article{Huang_2021,
title = {EGFI: drug–drug interaction extraction and generation with fusion of enriched entity and sentence information},
volume = {23},
issn = {1477-4054},
url = {http://dx.doi.org/10.1093/bib/bbab451},
doi = {10.1093/bib/bbab451},
number = {1},
journal = {Briefings in Bioinformatics},
publisher = {Oxford University Press (OUP)},
author = {Huang, Lei and Lin, Jiecong and Li, Xiangtao and Song, Linqi and Zheng, Zetian and Wong, Ka-Chun},
year = {2021},
month = nov
}
Li, X., Li, S., Huang, L., Zhang, S., & Wong, K.-chun. (2021). High-throughput single-cell RNA-seq data imputation and characterization with surrogate-assisted automated deep learning. Briefings in Bioinformatics, 23(1). http://dx.doi.org/10.1093/bib/bbab368
@article{Li_2023,
title = {High-throughput single-cell RNA-seq data imputation and characterization with surrogate-assisted automated deep learning},
volume = {23},
issn = {1477-4054},
url = {http://dx.doi.org/10.1093/bib/bbab368},
doi = {10.1093/bib/bbab368},
number = {1},
journal = {Briefings in Bioinformatics},
publisher = {Oxford University Press (OUP)},
author = {Li, Xiangtao and Li, Shaochuan and Huang, Lei and Zhang, Shixiong and Wong, Ka-chun},
year = {2021},
month = sep
}
Hou, Z., Yang, Y., Li, H., Wong, K.-chun, & Li, X. (2021). iDeepSubMito: identification of protein submitochondrial localization with deep learning. Briefings in Bioinformatics, 22(6). http://dx.doi.org/10.1093/bib/bbab288
@article{Hou_2021,
title = {iDeepSubMito: identification of protein submitochondrial localization with deep learning},
volume = {22},
issn = {1477-4054},
url = {http://dx.doi.org/10.1093/bib/bbab288},
doi = {10.1093/bib/bbab288},
number = {6},
journal = {Briefings in Bioinformatics},
publisher = {Oxford University Press (OUP)},
author = {Hou, Zilong and Yang, Yuning and Li, Hui and Wong, Ka-chun and Li, Xiangtao},
year = {2021},
month = jul
}
Yu, Z., Bian, C., Liu, G., Zhang, S., Wong, K.-C., & Li, X. (2021). Elucidating transcriptomic profiles from single-cell RNA sequencing data using nature-inspired compressed sensing. Briefings in Bioinformatics, 22(5). http://dx.doi.org/10.1093/bib/bbab125
@article{Yu_2021,
title = {Elucidating transcriptomic profiles from single-cell RNA sequencing data using nature-inspired compressed sensing},
volume = {22},
issn = {1477-4054},
url = {http://dx.doi.org/10.1093/bib/bbab125},
doi = {10.1093/bib/bbab125},
number = {5},
journal = {Briefings in Bioinformatics},
publisher = {Oxford University Press (OUP)},
author = {Yu, Zhuohan and Bian, Chuang and Liu, Genggeng and Zhang, Shixiong and Wong, Ka-Chun and Li, Xiangtao},
year = {2021},
month = apr
}
Li, X., Zhang, S., & Wong, K.-C. (2021). Deep embedded clustering with multiple objectives on scRNA-seq data. Briefings in Bioinformatics, 22(5). http://dx.doi.org/10.1093/bib/bbab090
@article{Li_2024,
title = {Deep embedded clustering with multiple objectives on scRNA-seq data},
volume = {22},
issn = {1477-4054},
url = {http://dx.doi.org/10.1093/bib/bbab090},
doi = {10.1093/bib/bbab090},
number = {5},
journal = {Briefings in Bioinformatics},
publisher = {Oxford University Press (OUP)},
author = {Li, Xiangtao and Zhang, Shixiong and Wong, Ka-Chun},
year = {2021},
month = apr
}
Yang, Y., Li, S., Wang, Y., Ma, Z., Wong, K.-C., & Li, X. (2021). Identification of haploinsufficient genes from epigenomic data using deep forest. Briefings in Bioinformatics, 22(5). http://dx.doi.org/10.1093/bib/bbaa393
@article{Yang_2021,
title = {Identification of haploinsufficient genes from epigenomic data using deep forest},
volume = {22},
issn = {1477-4054},
url = {http://dx.doi.org/10.1093/bib/bbaa393},
doi = {10.1093/bib/bbaa393},
number = {5},
journal = {Briefings in Bioinformatics},
publisher = {Oxford University Press (OUP)},
author = {Yang, Yuning and Li, Shaochuan and Wang, Yunhe and Ma, Zhiqiang and Wong, Ka-Chun and Li, Xiangtao},
year = {2021},
month = jan
}
Li, X., Li, S., Wang, Y., Zhang, S., & Wong, K.-C. (2020). Identification of pan-cancer Ras pathway activation with deep learning. Briefings in Bioinformatics, 22(4). http://dx.doi.org/10.1093/bib/bbaa258
@article{Li_2020,
title = {Identification of pan-cancer Ras pathway activation with deep learning},
volume = {22},
issn = {1477-4054},
url = {http://dx.doi.org/10.1093/bib/bbaa258},
doi = {10.1093/bib/bbaa258},
number = {4},
journal = {Briefings in Bioinformatics},
publisher = {Oxford University Press (OUP)},
author = {Li, Xiangtao and Li, Shaochuan and Wang, Yunhe and Zhang, Shixiong and Wong, Ka-Chun},
year = {2020},
month = oct
}
Yang, Y., Hou, Z., Ma, Z., Li, X., & Wong, K.-C. (2020). iCircRBP-DHN: identification of circRNA-RBP interaction sites using deep hierarchical network. Briefings in Bioinformatics, 22(4). http://dx.doi.org/10.1093/bib/bbaa274
@article{Yang_2020,
title = {iCircRBP-DHN: identification of circRNA-RBP interaction sites using deep hierarchical network},
volume = {22},
issn = {1477-4054},
url = {http://dx.doi.org/10.1093/bib/bbaa274},
doi = {10.1093/bib/bbaa274},
number = {4},
journal = {Briefings in Bioinformatics},
publisher = {Oxford University Press (OUP)},
author = {Yang, Yuning and Hou, Zilong and Ma, Zhiqiang and Li, Xiangtao and Wong, Ka-Chun},
year = {2020},
month = oct
}
Li, X., & Wong, K.-C. (2018). Multiobjective Patient Stratification Using Evolutionary Multiobjective Optimization. IEEE Journal of Biomedical and Health Informatics, 22(5), 1619–1629. http://dx.doi.org/10.1109/jbhi.2017.2769711
@article{Li_2018,
title = {Multiobjective Patient Stratification Using Evolutionary Multiobjective Optimization},
volume = {22},
issn = {2168-2208},
url = {http://dx.doi.org/10.1109/jbhi.2017.2769711},
doi = {10.1109/jbhi.2017.2769711},
number = {5},
journal = {IEEE Journal of Biomedical and Health Informatics},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
author = {Li, Xiangtao and Wong, Ka-Chun},
year = {2018},
month = sep,
pages = {1619–1629}
}
Li, X., & Li, M. (2015). Multiobjective Local Search Algorithm-Based Decomposition for Multiobjective Permutation Flow Shop Scheduling Problem. IEEE Transactions on Engineering Management, 62(4), 544–557. http://dx.doi.org/10.1109/tem.2015.2453264
@article{Li_2015,
title = {Multiobjective Local Search Algorithm-Based Decomposition for Multiobjective Permutation Flow Shop Scheduling Problem},
volume = {62},
issn = {1558-0040},
url = {http://dx.doi.org/10.1109/tem.2015.2453264},
doi = {10.1109/tem.2015.2453264},
number = {4},
journal = {IEEE Transactions on Engineering Management},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
author = {Li, Xiangtao and Li, Mingjie},
year = {2015},
month = nov,
pages = {544–557}
}
Li, X., & Ma, S. (2017). Multiobjective Discrete Artificial Bee Colony Algorithm for Multiobjective Permutation Flow Shop Scheduling Problem With Sequence Dependent Setup Times. IEEE Transactions on Engineering Management, 64(2), 149–165. http://dx.doi.org/10.1109/tem.2016.2645790
@article{Li_2017,
title = {Multiobjective Discrete Artificial Bee Colony Algorithm for Multiobjective Permutation Flow Shop Scheduling Problem With Sequence Dependent Setup Times},
volume = {64},
issn = {1558-0040},
url = {http://dx.doi.org/10.1109/tem.2016.2645790},
doi = {10.1109/tem.2016.2645790},
number = {2},
journal = {IEEE Transactions on Engineering Management},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
author = {Li, Xiangtao and Ma, Shijing},
year = {2017},
month = may,
pages = {149–165}
}
Li, X., Zhang, S., & Wong, K.-C. (2021). Evolving Transcriptomic Profiles From Single-Cell RNA-Seq Data Using Nature-Inspired Multiobjective Optimization. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(6), 2445–2458. http://dx.doi.org/10.1109/tcbb.2020.2971993
@article{Li_2025,
title = {Evolving Transcriptomic Profiles From Single-Cell RNA-Seq Data Using Nature-Inspired Multiobjective Optimization},
volume = {18},
issn = {2374-0043},
url = {http://dx.doi.org/10.1109/tcbb.2020.2971993},
doi = {10.1109/tcbb.2020.2971993},
number = {6},
journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
author = {Li, Xiangtao and Zhang, Shixiong and Wong, Ka-Chun},
year = {2021},
month = nov,
pages = {2445–2458}
}
Wang, Y., Ma, Z., Wong, K.-C., & Li, X. (2021). Evolving Multiobjective Cancer Subtype Diagnosis From Cancer Gene Expression Data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(6), 2431–2444. http://dx.doi.org/10.1109/tcbb.2020.2974953
@article{Wang_2021,
title = {Evolving Multiobjective Cancer Subtype Diagnosis From Cancer Gene Expression Data},
volume = {18},
issn = {2374-0043},
url = {http://dx.doi.org/10.1109/tcbb.2020.2974953},
doi = {10.1109/tcbb.2020.2974953},
number = {6},
journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
author = {Wang, Yunhe and Ma, Zhiqiang and Wong, Ka-Chun and Li, Xiangtao},
year = {2021},
month = nov,
pages = {2431–2444}
}
Li, X., & Wong, K.-C. (2019). Single-Cell RNA Sequencing Data Interpretation by Evolutionary Multiobjective Clustering. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 1–1. http://dx.doi.org/10.1109/tcbb.2019.2906601
@article{Li_2026,
title = {Single-Cell RNA Sequencing Data Interpretation by Evolutionary Multiobjective Clustering},
issn = {2374-0043},
url = {http://dx.doi.org/10.1109/tcbb.2019.2906601},
doi = {10.1109/tcbb.2019.2906601},
journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
author = {Li, Xiangtao and Wong, Ka-Chun},
year = {2019},
pages = {1–1}
}
Li, X., Zhang, S., & Wong, K.-C. (2018). Nature-Inspired Multiobjective Epistasis Elucidation from Genome-Wide Association Studies. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 1–1. http://dx.doi.org/10.1109/tcbb.2018.2849759
@article{Li_2027,
title = {Nature-Inspired Multiobjective Epistasis Elucidation from Genome-Wide Association Studies},
issn = {2374-0043},
url = {http://dx.doi.org/10.1109/tcbb.2018.2849759},
doi = {10.1109/tcbb.2018.2849759},
journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
author = {Li, Xiangtao and Zhang, Shixiong and Wong, Ka-Chun},
year = {2018},
pages = {1–1}
}
Li, X., & Wong, K.-C. (2019). Elucidating Genome-Wide Protein-RNA Interactions Using Differential Evolution. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 16(1), 272–282. http://dx.doi.org/10.1109/tcbb.2017.2776224
@article{Li_2028,
title = {Elucidating Genome-Wide Protein-RNA Interactions Using Differential Evolution},
volume = {16},
issn = {2374-0043},
url = {http://dx.doi.org/10.1109/tcbb.2017.2776224},
doi = {10.1109/tcbb.2017.2776224},
number = {1},
journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
author = {Li, Xiangtao and Wong, Ka-Chun},
year = {2019},
month = jan,
pages = {272–282}
}
Li, X., & Wong, K.-C. (2018). A Comparative Study for Identifying the Chromosome-Wide Spatial Clusters from High-Throughput Chromatin Conformation Capture Data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 15(3), 774–787. http://dx.doi.org/10.1109/tcbb.2017.2684800
@article{Li_2029,
title = {A Comparative Study for Identifying the Chromosome-Wide Spatial Clusters from High-Throughput Chromatin Conformation Capture Data},
volume = {15},
issn = {1545-5963},
url = {http://dx.doi.org/10.1109/tcbb.2017.2684800},
doi = {10.1109/tcbb.2017.2684800},
number = {3},
journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
author = {Li, Xiangtao and Wong, Ka-Chun},
year = {2018},
month = may,
pages = {774–787}
}
Wong, K.-C., Yan, S., Lin, Q., Li, X., & Peng, C. (2020). Deleterious Non-Synonymous Single Nucleotide Polymorphism Predictions on Human Transcription Factors. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(1), 327–333. http://dx.doi.org/10.1109/tcbb.2018.2882548
@article{Wong_2020,
title = {Deleterious Non-Synonymous Single Nucleotide Polymorphism Predictions on Human Transcription Factors},
volume = {17},
issn = {2374-0043},
url = {http://dx.doi.org/10.1109/tcbb.2018.2882548},
doi = {10.1109/tcbb.2018.2882548},
number = {1},
journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
author = {Wong, Ka-Chun and Yan, Shankai and Lin, Qiuzhen and Li, Xiangtao and Peng, Chengbin},
year = {2020},
month = jan,
pages = {327–333}
}
Li, X., & Yin, M. (2013). Multiobjective Binary Biogeography Based Optimization for Feature Selection Using Gene Expression Data. IEEE Transactions on NanoBioscience, 12(4), 343–353. http://dx.doi.org/10.1109/tnb.2013.2294716
@article{Li_2013,
title = {Multiobjective Binary Biogeography Based Optimization for Feature Selection Using Gene Expression Data},
volume = {12},
issn = {1558-2639},
url = {http://dx.doi.org/10.1109/tnb.2013.2294716},
doi = {10.1109/tnb.2013.2294716},
number = {4},
journal = {IEEE Transactions on NanoBioscience},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
author = {Li, Xiangtao and Yin, Minghao},
year = {2013},
month = dec,
pages = {343–353}
}
Li, X., Ma, S., & Wong, K.-C. (2017). Evolving Spatial Clusters of Genomic Regions From High-Throughput Chromatin Conformation Capture Data. IEEE Transactions on NanoBioscience, 16(6), 400–407. http://dx.doi.org/10.1109/tnb.2017.2725991
@article{Li_2030,
title = {Evolving Spatial Clusters of Genomic Regions From High-Throughput Chromatin Conformation Capture Data},
volume = {16},
issn = {1558-2639},
url = {http://dx.doi.org/10.1109/tnb.2017.2725991},
doi = {10.1109/tnb.2017.2725991},
number = {6},
journal = {IEEE Transactions on NanoBioscience},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
author = {Li, Xiangtao and Ma, Shijing and Wong, Ka-Chun},
year = {2017},
month = sep,
pages = {400–407}
}
Wang, Y., Liu, B., Ma, Z., Wong, K.-C., & Li, X. (2019). Nature-Inspired Multiobjective Cancer Subtype Diagnosis. IEEE Journal of Translational Engineering in Health and Medicine, 7, 1–12. http://dx.doi.org/10.1109/jtehm.2019.2891746
@article{Wang_2019,
title = {Nature-Inspired Multiobjective Cancer Subtype Diagnosis},
volume = {7},
issn = {2168-2372},
url = {http://dx.doi.org/10.1109/jtehm.2019.2891746},
doi = {10.1109/jtehm.2019.2891746},
journal = {IEEE Journal of Translational Engineering in Health and Medicine},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
author = {Wang, Yunhe and Liu, Bo and Ma, Zhiqiang and Wong, Ka-Chun and Li, Xiangtao},
year = {2019},
pages = {1–12}
}
Book
(2019). In Natural Computing for Unsupervised Learning. Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-98566-4
@book{Li,
booktitle = {Natural Computing for Unsupervised Learning},
isbn = {9783319985664},
issn = {2522-8498},
url = {http://dx.doi.org/10.1007/978-3-319-98566-4},
doi = {10.1007/978-3-319-98566-4},
journal = {Unsupervised and Semi-Supervised Learning},
publisher = {Springer International Publishing},
year = {2019}
}