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  1. Article ; Online: Tessellating the Latent Space for Non-Adversarial Generative Auto-Encoders.

    Gai, Kuo / Zhang, Shihua

    IEEE transactions on pattern analysis and machine intelligence

    2024  Volume 46, Issue 2, Page(s) 780–792

    Abstract: Non-adversarial generative models are relatively easy to train and have less mode collapse than adversarial models. However, they are not very accurate in approximating the target distribution in latent space because they don't have a discriminator. To ... ...

    Abstract Non-adversarial generative models are relatively easy to train and have less mode collapse than adversarial models. However, they are not very accurate in approximating the target distribution in latent space because they don't have a discriminator. To this end, we develop a novel divide-and-conquer model called Tessellated Wasserstein Auto-Encoders (TWAE) which has less statistical error in approximating the target distribution. TWAE tessellates the support of the target distribution into a given number of regions using the centroidal Voronoi tessellation (CVT) technique and designs data batches according to the tessellation instead of random shuffling for accurate computation of discrepancy. Theoretically, we demonstrate that the error in estimating the discrepancy decreases as the number of samples n and the regions m of the tessellation increase at rates of [Formula: see text] and [Formula: see text], respectively. TWAE is very flexible to different non-adversarial metrics and can significantly enhance their generative performance in terms of Fréchet inception distance (FID) compared to existing ones. Furthermore, numerical results demonstrate that TWAE is competitive to the adversarial model and shows powerful generative ability.
    Language English
    Publishing date 2024-01-08
    Publishing country United States
    Document type Journal Article
    ISSN 1939-3539
    ISSN (online) 1939-3539
    DOI 10.1109/TPAMI.2023.3325282
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: ConvMatch: Rethinking Network Design for Two-View Correspondence Learning.

    Zhang, Shihua / Ma, Jiayi

    IEEE transactions on pattern analysis and machine intelligence

    2024  Volume 46, Issue 5, Page(s) 2920–2935

    Abstract: Multilayer perceptron (MLP) has become the de facto backbone in two-view correspondence learning, for it can extract effective deep features from unordered correspondences individually. However, the problem of natively lacking context information limits ... ...

    Abstract Multilayer perceptron (MLP) has become the de facto backbone in two-view correspondence learning, for it can extract effective deep features from unordered correspondences individually. However, the problem of natively lacking context information limits its performance although many context-capturing modules are appended in the follow-up studies. In this paper, from a novel perspective, we design a correspondence learning network called ConvMatch that for the first time can leverage a convolutional neural network (CNN) as the backbone, inherently capable of context aggregation. Specifically, with the observation that sparse motion vectors and a dense motion field can be converted into each other with interpolating and sampling, we regularize the putative motion vectors by estimating the dense motion field implicitly, then rectify the errors caused by outliers in local areas with CNN, and finally obtain correct motion vectors from the rectified motion field. Moreover, we propose global information injection and bilateral convolution, to fit the overall spatial transformation better and accommodate the discontinuities of the motion field in case of large scene disparity. Extensive experiments reveal that ConvMatch consistently outperforms state-of-the-arts for relative pose estimation, homography estimation, and visual localization.
    Language English
    Publishing date 2024-04-03
    Publishing country United States
    Document type Journal Article
    ISSN 1939-3539
    ISSN (online) 1939-3539
    DOI 10.1109/TPAMI.2023.3334515
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Computational Methods for Subtyping of Tumors and Their Applications for Deciphering Tumor Heterogeneity.

    Zhang, Shihua

    Methods in molecular biology (Clifton, N.J.)

    2018  Volume 1878, Page(s) 193–207

    Abstract: With the rapid development of deep sequencing technologies, many programs are generating multi-platform genomic profiles (e.g., somatic mutation, DNA methylation, and gene expression) for a large number of tumors. This activity has provided unique ... ...

    Abstract With the rapid development of deep sequencing technologies, many programs are generating multi-platform genomic profiles (e.g., somatic mutation, DNA methylation, and gene expression) for a large number of tumors. This activity has provided unique opportunities and challenges to stratify tumors and decipher tumor heterogeneity. In this chapter, we summarize several computational methods to address the challenge of tumor stratification with different types of genomic data. We further introduce their applications in emerging large-scale genomic data to show their effectiveness in deciphering tumor heterogeneity and clinical relevance.
    MeSH term(s) Computational Biology/methods ; Gene Expression/genetics ; Gene Expression Profiling/methods ; Genomics/methods ; High-Throughput Nucleotide Sequencing ; Humans ; Mutation/genetics ; Neoplasms/genetics
    Language English
    Publishing date 2018-10-30
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Review
    ISSN 1940-6029
    ISSN (online) 1940-6029
    DOI 10.1007/978-1-4939-8868-6_11
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Adversarial Information Bottleneck.

    Zhai, Penglong / Zhang, Shihua

    IEEE transactions on neural networks and learning systems

    2022  Volume PP

    Abstract: The information bottleneck (IB) principle has been adopted to explain deep learning in terms of information compression and prediction, which are balanced by a tradeoff hyperparameter. How to optimize the IB principle for better robustness and figure out ...

    Abstract The information bottleneck (IB) principle has been adopted to explain deep learning in terms of information compression and prediction, which are balanced by a tradeoff hyperparameter. How to optimize the IB principle for better robustness and figure out the effects of compression through the tradeoff hyperparameter are two challenging problems. Previous methods attempted to optimize the IB principle by introducing random noise into learning the representation and achieved the state-of-the-art performance in the nuisance information compression and semantic information extraction. However, their performance on resisting adversarial perturbations is far less impressive. To this end, we propose an adversarial IB (AIB) method without any explicit assumptions about the underlying distribution of the representations, which can be optimized effectively by solving a min-max optimization problem. Numerical experiments on synthetic and real-world datasets demonstrate its effectiveness on learning more invariant representations and mitigating adversarial perturbations compared to several competing IB methods. In addition, we analyze the adversarial robustness of diverse IB methods contrasting with their IB curves and reveal that IB models with the hyperparameter β corresponding to the knee point in the IB curve achieve the best tradeoff between compression and prediction and has the best robustness against various attacks.
    Language English
    Publishing date 2022-05-20
    Publishing country United States
    Document type Journal Article
    ISSN 2162-2388
    ISSN (online) 2162-2388
    DOI 10.1109/TNNLS.2022.3172986
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder.

    Dong, Kangning / Zhang, Shihua

    Nature communications

    2022  Volume 13, Issue 1, Page(s) 1739

    Abstract: Recent advances in spatially resolved transcriptomics have enabled comprehensive measurements of gene expression patterns while retaining the spatial context of the tissue microenvironment. Deciphering the spatial context of spots in a tissue needs to ... ...

    Abstract Recent advances in spatially resolved transcriptomics have enabled comprehensive measurements of gene expression patterns while retaining the spatial context of the tissue microenvironment. Deciphering the spatial context of spots in a tissue needs to use their spatial information carefully. To this end, we develop a graph attention auto-encoder framework STAGATE to accurately identify spatial domains by learning low-dimensional latent embeddings via integrating spatial information and gene expression profiles. To better characterize the spatial similarity at the boundary of spatial domains, STAGATE adopts an attention mechanism to adaptively learn the similarity of neighboring spots, and an optional cell type-aware module through integrating the pre-clustering of gene expressions. We validate STAGATE on diverse spatial transcriptomics datasets generated by different platforms with different spatial resolutions. STAGATE could substantially improve the identification accuracy of spatial domains, and denoise the data while preserving spatial expression patterns. Importantly, STAGATE could be extended to multiple consecutive sections to reduce batch effects between sections and extracting three-dimensional (3D) expression domains from the reconstructed 3D tissue effectively.
    MeSH term(s) Cluster Analysis ; Transcriptome/genetics
    Language English
    Publishing date 2022-04-01
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-022-29439-6
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: High-density generation of spatial transcriptomics with STAGE.

    Li, Shang / Gai, Kuo / Dong, Kangning / Zhang, Yiyang / Zhang, Shihua

    Nucleic acids research

    2024  

    Abstract: Spatial transcriptome technologies have enabled the measurement of gene expression while maintaining spatial location information for deciphering the spatial heterogeneity of biological tissues. However, they were heavily limited by the sparse spatial ... ...

    Abstract Spatial transcriptome technologies have enabled the measurement of gene expression while maintaining spatial location information for deciphering the spatial heterogeneity of biological tissues. However, they were heavily limited by the sparse spatial resolution and low data quality. To this end, we develop a spatial location-supervised auto-encoder generator STAGE for generating high-density spatial transcriptomics (ST). STAGE takes advantage of the customized supervised auto-encoder to learn continuous patterns of gene expression in space and generate high-resolution expressions for given spatial coordinates. STAGE can improve the low quality of spatial transcriptome data and smooth the generated manifold of gene expression through the de-noising function on the latent codes of the auto-encoder. Applications to four ST datasets, STAGE has shown better recovery performance for down-sampled data than existing methods, revealed significant tissue structure specificity, and enabled robust identification of spatially informative genes and patterns. In addition, STAGE can be extended to three-dimensional (3D) stacked ST data for generating gene expression at any position between consecutive sections for shaping high-density 3D ST configuration.
    Language English
    Publishing date 2024-04-22
    Publishing country England
    Document type Journal Article
    ZDB-ID 186809-3
    ISSN 1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
    ISSN (online) 1362-4962 ; 1362-4954
    ISSN 0301-5610 ; 0305-1048
    DOI 10.1093/nar/gkae294
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 1067: Show issues Location:
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  7. Article ; Online: Integrating spatial transcriptomics data across different conditions, technologies and developmental stages.

    Zhou, Xiang / Dong, Kangning / Zhang, Shihua

    Nature computational science

    2023  Volume 3, Issue 10, Page(s) 894–906

    Abstract: With the rapid generation of spatial transcriptomics (ST) data, integrative analysis of multiple ST datasets from different conditions, technologies and developmental stages is becoming increasingly important. Here we present a graph attention neural ... ...

    Abstract With the rapid generation of spatial transcriptomics (ST) data, integrative analysis of multiple ST datasets from different conditions, technologies and developmental stages is becoming increasingly important. Here we present a graph attention neural network called STAligner for integrating and aligning ST datasets, enabling spatially aware data integration, simultaneous spatial domain identification and downstream comparative analysis. We apply STAligner to ST datasets of the human cortex slices from different samples, the mouse olfactory bulb slices generated by two profiling technologies, the mouse hippocampus tissue slices under normal and Alzheimer's disease conditions, and the spatiotemporal atlases of mouse organogenesis. STAligner efficiently captures the shared tissue structures across different slices, the disease-related substructures and the dynamical changes during mouse embryonic development. In addition, the shared spatial domain and nearest-neighbor pairs identified by STAligner can be further considered as corresponding pairs to guide the three-dimensional reconstruction of consecutive slices, achieving more accurate local structure-guided registration than the existing method.
    MeSH term(s) Female ; Pregnancy ; Humans ; Animals ; Mice ; Embryonic Development/genetics ; Gene Expression Profiling ; Alzheimer Disease/genetics ; Awareness ; Cerebral Cortex
    Language English
    Publishing date 2023-10-12
    Publishing country United States
    Document type Journal Article
    ISSN 2662-8457
    ISSN (online) 2662-8457
    DOI 10.1038/s43588-023-00528-w
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Age-level bias correction in brain age prediction.

    Zhang, Biao / Zhang, Shuqin / Feng, Jianfeng / Zhang, Shihua

    NeuroImage. Clinical

    2023  Volume 37, Page(s) 103319

    Abstract: The predicted age difference (PAD) between an individual's predicted brain age and chronological age has been commonly viewed as a meaningful phenotype relating to aging and brain diseases. However, the systematic bias appears in the PAD achieved using ... ...

    Abstract The predicted age difference (PAD) between an individual's predicted brain age and chronological age has been commonly viewed as a meaningful phenotype relating to aging and brain diseases. However, the systematic bias appears in the PAD achieved using machine learning methods. Recent studies have designed diverse bias correction methods to eliminate it for further downstream studies. Strikingly, here we demonstrate that bias still exists in the PAD of samples with the same age even after kind of correction. Therefore, current PAD may not be taken as a reliable phenotype and more investigations are needed to solve this fundamental defect. To this end, we propose an age-level bias correction method and demonstrate its efficacy in numerical experiments.
    MeSH term(s) Magnetic Resonance Imaging/methods ; Brain/diagnostic imaging ; Machine Learning
    Language English
    Publishing date 2023-01-07
    Publishing country Netherlands
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2701571-3
    ISSN 2213-1582 ; 2213-1582
    ISSN (online) 2213-1582
    ISSN 2213-1582
    DOI 10.1016/j.nicl.2023.103319
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: A Python Package

    Zhang, Chihao / Zhang, Shihua / Li, Jingyi Jessica

    Journal of computational biology : a journal of computational molecular cell biology

    2023  Volume 30, Issue 11, Page(s) 1246–1249

    Language English
    Publishing date 2023-11-06
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2030900-4
    ISSN 1557-8666 ; 1066-5277
    ISSN (online) 1557-8666
    ISSN 1066-5277
    DOI 10.1089/cmb.2023.0191
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: SmartGate is a spatial metabolomics tool for resolving tissue structures.

    Xiao, Kaixuan / Wang, Yu / Dong, Kangning / Zhang, Shihua

    Briefings in bioinformatics

    2023  Volume 24, Issue 3

    Abstract: Imaging mass spectrometry (IMS) is one of the powerful tools in spatial metabolomics for obtaining metabolite data and probing the internal microenvironment of organisms. It has dramatically advanced the understanding of the structure of biological ... ...

    Abstract Imaging mass spectrometry (IMS) is one of the powerful tools in spatial metabolomics for obtaining metabolite data and probing the internal microenvironment of organisms. It has dramatically advanced the understanding of the structure of biological tissues and the drug treatment of diseases. However, the complexity of IMS data hinders the further acquisition of biomarkers and the study of certain specific activities of organisms. To this end, we introduce an artificial intelligence tool, SmartGate, to enable automatic peak selection and spatial structure identification in an iterative manner. SmartGate selects discriminative m/z features from the previous iteration by differential analysis and employs a graph attention autoencoder model to perform spatial clustering for tissue segmentation using the selected features. We applied SmartGate to diverse IMS data at multicellular or subcellular spatial resolutions and compared it with four competing methods to demonstrate its effectiveness. SmartGate can significantly improve the accuracy of spatial segmentation and identify biomarker metabolites based on tissue structure-guided differential analysis. For multiple consecutive IMS data, SmartGate can effectively identify structures with spatial heterogeneity by introducing three-dimensional spatial neighbor information.
    MeSH term(s) Artificial Intelligence ; Metabolomics/methods ; Biomarkers
    Chemical Substances Biomarkers
    Language English
    Publishing date 2023-04-15
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2068142-2
    ISSN 1477-4054 ; 1467-5463
    ISSN (online) 1477-4054
    ISSN 1467-5463
    DOI 10.1093/bib/bbad141
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 6262: Show issues Location:
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