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546 results on '"Cai, Hongmin"'

51. NeoaPred: a deep-learning framework for predicting immunogenic neoantigen based on surface and structural features of peptide–human leukocyte antigen complexes.

Author

Jiang, Dawei, Xi, Binbin, Tan, Wenchong, Chen, Zixi, Wei, Jinfen, Hu, Meiling, Lu, Xiaoyun, Chen, Dong, Cai, Hongmin, and Du, Hongli

Subjects
RECEIVER operating characteristic curves, HLA histocompatibility antigens, SOMATIC mutation, DEEP learning, CANCER cells
Abstract

Motivation Neoantigens, derived from somatic mutations in cancer cells, can elicit anti-tumor immune responses when presented to autologous T cells by human leukocyte antigen. Identifying immunogenic neoantigens is crucial for cancer immunotherapy development. However, the accuracy of current bioinformatic methods remains unsatisfactory. Surface and structural features of peptide–HLA class I (pHLA-I) complexes offer valuable insight into the immunogenicity of neoantigens. Results We present NeoaPred, a deep-learning framework for neoantigen prediction. NeoaPred accurately constructs pHLA-I complex structures, with 82.37% of the predicted structures showing an RMSD of < 1 Å. Using these structures, NeoaPred integrates differences in surface, structural, and atom group features between the mutant peptide and its wild-type counterpart to predict a foreignness score. This foreignness score is an effective factor for neoantigen prediction, achieving an AUROC (Area Under the Receiver Operating Characteristic Curve) of 0.81 and an AUPRC (Area Under the Precision-Recall Curve) of 0.54 in the test set, outperforming existing methods. Availability and implementation The source code is released under an Apache v2.0 license and is available at the GitHub repository (https://github.com/Dulab2020/NeoaPred). [ABSTRACT FROM AUTHOR]

Published
2024
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52. Detecting Brain State Changes by Geometric Deep Learning of Functional Dynamics on Riemannian Manifold

Author

Huang, Zhuobin, Cai, Hongmin, Dan, Tingting, Lin, Yi, Laurienti, Paul, Wu, Guorong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, de Bruijne, Marleen, editor, Cattin, Philippe C., editor, Cotin, Stéphane, editor, Padoy, Nicolas, editor, Speidel, Stefanie, editor, Zheng, Yefeng, editor, and Essert, Caroline, editor

Published
2021
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53. Incorporating Discrete Wavelet Transformation Decomposition Convolution into Deep Network to Achieve Light Training

Author

Tao, Guihua, Rong, Wentao, Weng, Wanlin, Dan, Tingting, Zhang, Bin, Cai, Hongmin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Farkaš, Igor, editor, Masulli, Paolo, editor, Otte, Sebastian, editor, and Wermter, Stefan, editor

Published
2021
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54. Discovering Spreading Pathways of Neuropathological Events in Alzheimer’s Disease Using Harmonic Wavelets

Author

Chen, Jiazhou, Yang, Defu, Cai, Hongmin, Styner, Martin, Wu, Guorong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Feragen, Aasa, editor, Sommer, Stefan, editor, Schnabel, Julia, editor, and Nielsen, Mads, editor

Published
2021
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55. Effective and Adaptive Refined Multi-metric Similarity Graph Fusion for Multi-view Clustering

Author

Rong, Wentao, Zhuo, Enhong, Tao, Guihua, Cai, Hongmin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Karlapalem, Kamal, editor, Cheng, Hong, editor, Ramakrishnan, Naren, editor, Agrawal, R. K., editor, Reddy, P. Krishna, editor, Srivastava, Jaideep, editor, and Chakraborty, Tanmoy, editor

Published
2021
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60. Estimating Common Harmonic Waves of Brain Networks on Stiefel Manifold

Author

Chen, Jiazhou, Han, Guoqiang, Cai, Hongmin, Ma, Junbo, Kim, Minjeong, Laurienti, Paul, Wu, Guorong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Martel, Anne L., editor, Abolmaesumi, Purang, editor, Stoyanov, Danail, editor, Mateus, Diana, editor, Zuluaga, Maria A., editor, Zhou, S. Kevin, editor, Racoceanu, Daniel, editor, and Joskowicz, Leo, editor

Published
2020
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64. Multiview Tensor Spectral Clustering via Co-Regularization

Author

Cai, Hongmin, Wang, Yu, Qi, Fei, Wang, Zhuoyao, and Cheung, Yiu-ming

Abstract

Graph-based multi-view clustering encodes multi-view data into sample affinities to find consensus representation, effectively overcoming heterogeneity across different views. However, traditional affinity measures tend to collapse as the feature dimension expands, posing challenges in estimating a unified alignment that reveals both cross-view and inner relationships. To tackle this challenge, we propose to achieve multi-view uniform clustering via consensus representation co-regularization. First, the sample affinities are encoded by both popular dyadic affinity and recent high-order affinities to comprehensively characterize spatial distributions of the HDLSS data. Second, a fused consensus representation is learned through aligning the multi-view low-dimensional representation by co-regularization. The learning of the fused representation is modeled by a high-order eigenvalue problem within manifold space to preserve the intrinsic connections and complementary correlations of original data. A numerical scheme via manifold minimization is designed to solve the high-order eigenvalue problem efficaciously. Experiments on eight HDLSS datasets demonstrate the effectiveness of our proposed method in comparison with the recent thirteen benchmark methods.

Published
2024
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66. Multiple-Crop Human Mesh Recovery with Contrastive Learning and Camera Consistency in A Single Image

Author

Nie, Yongwei, Liu, Changzhen, Long, Chengjiang, Zhang, Qing, Li, Guiqing, Cai, Hongmin, Nie, Yongwei, Liu, Changzhen, Long, Chengjiang, Zhang, Qing, Li, Guiqing, and Cai, Hongmin

Abstract

We tackle the problem of single-image Human Mesh Recovery (HMR). Previous approaches are mostly based on a single crop. In this paper, we shift the single-crop HMR to a novel multiple-crop HMR paradigm. Cropping a human from image multiple times by shifting and scaling the original bounding box is feasible in practice, easy to implement, and incurs neglectable cost, but immediately enriches available visual details. With multiple crops as input, we manage to leverage the relation among these crops to extract discriminative features and reduce camera ambiguity. Specifically, (1) we incorporate a contrastive learning scheme to enhance the similarity between features extracted from crops of the same human. (2) We also propose a crop-aware fusion scheme to fuse the features of multiple crops for regressing the target mesh. (3) We compute local cameras for all the input crops and build a camera-consistency loss between the local cameras, which reward us with less ambiguous cameras. Based on the above innovations, our proposed method outperforms previous approaches as demonstrated by the extensive experiments.

Published
2024

69. Achieving Accurate Segmentation of Nasopharyngeal Carcinoma in MR Images Through Recurrent Attention

Author

Huang, Jia-bin, Zhuo, Enhong, Li, Haojiang, Liu, Lizhi, Cai, Hongmin, Ou, Yangming, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Shen, Dinggang, editor, Liu, Tianming, editor, Peters, Terry M., editor, Staib, Lawrence H., editor, Essert, Caroline, editor, Zhou, Sean, editor, Yap, Pew-Thian, editor, and Khan, Ali, editor

Published
2019
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77. Coarse-to-fine Knowledge Graph Domain Adaptation based on Distantly-supervised Iterative Training

Author

Liao, Wenxiong, primary, Liu, Zhengliang, additional, Zhang, Yiyang, additional, Huang, Xiaoke, additional, Qi, Fei, additional, Ding, Siqi, additional, Ren, Hui, additional, Wu, Zihao, additional, Dai, Haixing, additional, Li, Sheng, additional, Wu, Lingfei, additional, Liu, Ninghao, additional, Li, Quanzheng, additional, Liu, Tianming, additional, Li, Xiang, additional, and Cai, Hongmin, additional

Published
2023
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78. A synthetic nanobody targeting RBD protects hamsters from SARS-CoV-2 infection

Author

Li, Tingting, Cai, Hongmin, Yao, Hebang, Zhou, Bingjie, Zhang, Ning, van Vlissingen, Martje Fentener, Kuiken, Thijs, Han, Wenyu, GeurtsvanKessel, Corine H., Gong, Yuhuan, Zhao, Yapei, Shen, Quan, Qin, Wenming, Tian, Xiao-Xu, Peng, Chao, Lai, Yanling, Wang, Yanxing, Hutter, Cedric A. J., Kuo, Shu-Ming, Bao, Juan, Liu, Caixuan, Wang, Yifan, Richard, Audrey S., Raoul, Hervé, Lan, Jiaming, Seeger, Markus A., Cong, Yao, Rockx, Barry, Wong, Gary, Bi, Yuhai, Lavillette, Dimitri, and Li, Dianfan

Published
2021
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85. Microfacet rendering with diffraction compensation.

Author

Yang, Xudong, Lyu, Aoran, Xian, Chuhua, and Cai, Hongmin

Abstract

The traditional microfacet rendering models usually only consider the straight propagation of light and do not take into account the diffraction effect when calculating the radiance of outgoing light. However, ignoring the energy generated by diffraction can lead to darker rendering results when the object's surface has many small details. To address this issue, we introduce a diffraction energy term in the microfacet model to compensate for the energy loss caused by diffraction. Starting from the Fresnel‐Kirchhoff diffraction theorem, we combine it with the Cook‐Torrance model. By incorporating the computed diffraction radiance into the outgoing radiance of the microfacet, we obtain a diffraction‐compensated BRDF (Bidirectional Reflectance Distribution Function) model. Experimental results demonstrate that our proposed method has a significant effect in compensating for outgoing light and produces more realistic rendering results. [ABSTRACT FROM AUTHOR]

Published
2024
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97. MDAGenera: An Efficient and Accurate Simulator for Multiple Displacement Amplification

Author

Huang, Weiheng, Cai, Hongmin, Shao, Wei, Xu, Bo, Li, Fuqiang, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huang, De-Shuang, editor, Bevilacqua, Vitoantonio, editor, and Premaratne, Prashan, editor

Published
2016
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98. VSGAN: Visual Saliency guided Generative Adversarial Network for data augmentation

Author

Zhang, Jun, Xian, Chuhua, Bruckert, Alexandre, Le Callet, Patrick, Li, Guiqin, Cai, Hongmin, South China University of Technology [Guangzhou] (SCUT), Nantes Université - Ecole Polytechnique de l'Université de Nantes (Nantes Univ - EPUN), Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ), Laboratoire des Sciences du Numérique de Nantes (LS2N), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-École Centrale de Nantes (Nantes Univ - ECN), Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes université - UFR des Sciences et des Techniques (Nantes univ - UFR ST), Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ), Image Perception Interaction (LS2N - équipe IPI), Nantes Université (Nantes Univ)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST)

Subjects
Visual saliency, image inpainting, GAN, data augmentation, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

Deep learning approaches have allowed for a great leap in the performances of visual saliency models. However, the lack of annotated data remains the main challenge for visual saliency prediction. In this paper, we leverage image inpainting methods to synthesize augmented images, which is done by completing the weakly-salient areas, and propose a Visual Saliency guided Generative Adversarial Network (VSGAN) that contains a dual encoder to extract multiscale features and a generator equipped with visual saliency guided modulation to synthesize high fidelity and diversity results. Extensive experimental results show that our method outperforms state-of-the-art methods for image inpainting on visual saliency datasets, and demonstrate the effectiveness of VSGAN for visual saliency data augmentation both quantitatively and qualitatively. CCS CONCEPTS • Computing methodologies → Interest point and salient region detections.

Published
2023

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