TransMut: a program to predict HLA-I peptide binding and optimize mutated peptides for vaccine design by the Transformer-derived self-attention model

Chu, Yanyi; Zhang, Yan; Wang, Qiankun; Zhang, Lingfeng; Wang, Xuhong; Wang, Yanjing; Wang, Jianmin; Jiang, Xue; Salahub, Dennis R.; Xiong, Yi; Wei, Dong‐Qing

doi:10.21203/rs.3.rs-785618/v1

Cited by 4 publications

(6 citation statements)

References 75 publications

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“…Transformer has achieved impressive success in machine translation tasks [46]. It also performs outstandingly to solve numerous high-level vision problems [63,10,47,50]…”

Section: Related Workmentioning

confidence: 99%

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School of Mechanical Engineering, Shanghai Jiao Tong University, 200240, Shanghai, China

Zhuang

Huang

et al. 2019

Mathematical Biosciences and Engineering

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The crystallization kinetics and melting behavior of nylon 10,10 in neat nylon 10,10 and in nylon 10,10 -montmorillonite (MMT) nanocomposites were systematically investigated by differential scanning calorimetry. The crystallization kinetics results show that the addition of MMT facilitated the crystallization of nylon 10,10 as a heterophase nucleating agent; however, when the content of MMT was high, the physical hindrance of MMT layers to the motion of nylon 10,10 chains retarded the crystallization of nylon 10,10, which was also confirmed by polarized optical microscopy. However, both nylon 10,10 and nylon 10,10 -MMT nanocomposites exhibited multiple melting be-havior under isothermal and nonisothermal crystallization conditions. The temperature of the lower melting peak (peak I) was independent of MMT content and almost remained constant; however, the temperature of the highest melting peak (peak II) decreased with increasing MMT content due to the physical hindrance of MMT layers to the motion of nylon 10,10 chains.

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“…Transformer has achieved impressive success in machine translation tasks [46]. It also performs outstandingly to solve numerous high-level vision problems [63,10,47,50]…”

Section: Related Workmentioning

confidence: 99%

“…Transformer has shown state-of-the-art performance on high-level vision tasks [51,2,63,26,10,47,50]. The SA mechanism has great power to capture content-dependent global representations while modeling long-distance relationships.…”

Section: Introductionmentioning

confidence: 99%

School of Mechanical Engineering, Shanghai Jiao Tong University, 200240, Shanghai, China

Zhuang

Huang

et al. 2019

Mathematical Biosciences and Engineering

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“…Addressing the quadratic computational complexity of the original ViT's self-attention mechanism, Liu et al designed an approach that employs window shifting for local self-attention within windows, promoting cross-window interactions and obtaining gratifying results. Recent works [13,14,15,16,17] have shifted their focus away from image classification to more advanced computer vision domains.…”

Section: Transformermentioning

confidence: 99%

A low-light-level image enhancement algorithm combining Retinex and Transformer

Zhao,

2024

International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024)

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“…ViT also uses one extra class token to aggregate information from the entire sequence of the patch tokens. Although the class token has been removed in a number of recent transformer methods [7,8,29], this work will underline its importance for weakly supervised semantic segmentation.…”

Section: Introductionmentioning

confidence: 99%

Multi-class Token Transformer for Weakly Supervised Semantic Segmentation

Ouyang

Bennamoun

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

160

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This paper proposes a new transformer-based framework to learn class-specific object localization maps as pseudo labels for weakly supervised semantic segmentation (WSSS). Inspired by the fact that the attended regions of the one-class token in the standard vision transformer can be leveraged to form a class-agnostic localization map, we investigate if the transformer model can also effectively capture class-specific attention for more discriminative object localization by learning multiple class tokens within the transformer. To this end, we propose a Multi-class Token Transformer, termed as MCTformer, which uses multiple class tokens to learn interactions between the class tokens and the patch tokens. The proposed MCTformer can successfully produce class-discriminative object localization maps from the class-to-patch attentions corresponding to different class tokens. We also propose to use a patchlevel pairwise affinity, which is extracted from the patchto-patch transformer attention, to further refine the localization maps. Moreover, the proposed framework is shown to fully complement the Class Activation Mapping (CAM) method, leading to remarkably superior WSSS results on the PASCAL VOC and MS COCO datasets. These results underline the importance of the class token for WSSS. 1

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TransMut: a program to predict HLA-I peptide binding and optimize mutated peptides for vaccine design by the Transformer-derived self-attention model

Cited by 4 publications

References 75 publications

School of Mechanical Engineering, Shanghai Jiao Tong University, 200240, Shanghai, China

School of Mechanical Engineering, Shanghai Jiao Tong University, 200240, Shanghai, China

A low-light-level image enhancement algorithm combining Retinex and Transformer

Multi-class Token Transformer for Weakly Supervised Semantic Segmentation

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