Anti-cancer Peptide Recognition Based on Grouped Sequence and Spatial Dimension Integrated Networks

You, Hongfeng; Yu, Long; Tian, Shengwei; Ma, Xiang; Xing, Yan; Song, Jing; Wu, Weidong

doi:10.1007/s12539-021-00481-0

Cited by 8 publications

(8 citation statements)

References 29 publications

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“…For further verifying the effectiveness of our method, we compared ACPPfel with the existing methods including ACP-DL ( Yi et al, 2019 ), DeepACPpred ( Lane and Kahanda, 2021 ), ACP-MHCNN ( Ahmed et al, 2021 ), GRCI-Net ( You et al, 2022 ), StackACPred ( Mishra et al, 2019 ) on the cross-validation datasets and iACP ( Chen et al, 2016 ), PEPred-Suite ( Wei et al, 2019 ), ACPpred-Fuse ( Rao et al, 2020b ), ACPred-FL ( Wei et al, 2018 ), ACPred ( Schaduangrat et al, 2019 ), AntiCP ( Kumar and Li, 2017 ), DeepACPpred, AntiCP_2.0 ( Agrawal et al, 2021b ), iACP-DRLF ( Lv et al, 2021b ), ME-ACP ( Feng et al, 2022 ) on alternative independent datasets.…”

Section: Resultsmentioning

confidence: 99%

“…For further verifying the effectiveness of our method, we compared ACPPfel with the existing methods including ACP-DL (Yi et al, 2019), DeepACPpred (Lane and Kahanda, 2021), ACP-MHCNN (Ahmed et al, 2021), GRCI-Net (You et al, 2022), The ROC Curve performance of independent validation dataset of ACP740 dataset. The ROC Curve performance of independent validation dataset of ACPfel dataset.…”

Section: Comparison With the State-of-the-art Approachesmentioning

confidence: 99%

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ACPPfel: Explainable deep ensemble learning for anticancer peptides prediction based on feature optimization

Liu,

Wu,

et al. 2024

Front. Genet.

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Background: Cancer is a significant global health problem that continues to cause a high number of deaths worldwide. Traditional cancer treatments often come with risks that can compromise the functionality of vital organs. As a potential alternative to these conventional therapies, Anticancer peptides (ACPs) have garnered attention for their small size, high specificity, and reduced toxicity, making them as a promising option for cancer treatments.Methods: However, the process of identifying effective ACPs through wet-lab screening experiments is time-consuming and requires a lot of labor. To overcome this challenge, a deep ensemble learning method is constructed to predict anticancer peptides (ACPs) in this study. To evaluate the reliability of the framework, four different datasets are used in this study for training and testing. During the training process of the model, integration of feature selection methods, feature dimensionality reduction measures, and optimization of the deep ensemble model are carried out. Finally, we explored the interpretability of features that affected the final prediction results and built a web server platform to facilitate anticancer peptides prediction, which can be used by all researchers for further studies. This web server can be accessed at http://lmylab.online:5001/.Results: The result of this study achieves an accuracy rate of 98.53% and an AUC (Area under Curve) value of 0.9972 on the ACPfel dataset, it has improvements on other datasets as well.

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Section: Resultsmentioning

confidence: 99%

“…For further verifying the effectiveness of our method, we compared ACPPfel with the existing methods including ACP-DL (Yi et al, 2019), DeepACPpred (Lane and Kahanda, 2021), ACP-MHCNN (Ahmed et al, 2021), GRCI-Net (You et al, 2022), The ROC Curve performance of independent validation dataset of ACP740 dataset. The ROC Curve performance of independent validation dataset of ACPfel dataset.…”

Section: Comparison With the State-of-the-art Approachesmentioning

confidence: 99%

ACPPfel: Explainable deep ensemble learning for anticancer peptides prediction based on feature optimization

Liu,

Wu,

et al. 2024

Front. Genet.

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“…To demonstrate the effectiveness of our designed model, we compared ACP-BC with other state-of-the-art methods. We evaluated the performance of ACP-DA [47], ACP-DL [43], GRCI-Net [50], DeepACPpred [51], ACP-MHCNN [40], ACP-check [49], and other methods using the same ACP740 and ACP240 datasets for a fair comparison. Compared to ACP-DA, our model achieved better results with the combined use of two feature enhancement methods.…”

Section: Comparison Of Existing Methodsmentioning

confidence: 99%

“…Zhu et al [49] developed the ACP-check model, which uses LSTM networks to extract time-dependent information from peptide sequences for anticancer peptides to be identified effectively. You et al [50] fused the sparse matrix features of BPF and the k-mer sparse matrix to construct a new bidirectional short-term memory network, which achieves the prediction of anticancer peptides through two sets of dense network layers. The aforementioned studies demonstrate significant advancements in the field of computational peptide-based cancer research.…”

Section: Introductionmentioning

confidence: 99%

ACP-BC: A Model for Accurate Identification of Anticancer Peptides Based on Fusion Features of Bidirectional Long Short-Term Memory and Chemically Derived Information

Sun,

Hu,

Pang

et al. 2023

IJMS

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Anticancer peptides (ACPs) have been proven to possess potent anticancer activities. Although computational methods have emerged for rapid ACPs identification, their accuracy still needs improvement. In this study, we propose a model called ACP-BC, a three-channel end-to-end model that utilizes various combinations of data augmentation techniques. In the first channel, features are extracted from the raw sequence using a bidirectional long short-term memory network. In the second channel, the entire sequence is converted into a chemical molecular formula, which is further simplified using Simplified Molecular Input Line Entry System notation to obtain deep abstract features through a bidirectional encoder representation transformer (BERT). In the third channel, we manually selected four effective features according to dipeptide composition, binary profile feature, k-mer sparse matrix, and pseudo amino acid composition. Notably, the application of chemical BERT in predicting ACPs is novel and successfully integrated into our model. To validate the performance of our model, we selected two benchmark datasets, ACPs740 and ACPs240. ACP-BC achieved prediction accuracy with 87% and 90% on these two datasets, respectively, representing improvements of 1.3% and 7% compared to existing state-of-the-art methods on these datasets. Therefore, systematic comparative experiments have shown that the ACP-BC can effectively identify anticancer peptides.

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“… Rao et al (2020) designed a multi-view feature extraction model called ACPred-Fuse, which employed a total of 29 different sequence-based feature encoding methods and used RF to further select features. You et al ’s (2022) GRCI-Net model used binary structure and K-mer sparse matrix to extract the features of peptide sequences, and used principal component analysis to fuse the two features. The output was then fed into a classifier composed of bidirectional long short-term memory network (Bi-LSTM) and CNN.…”

Section: Introductionmentioning

confidence: 99%

AACFlow: an end-to-end model based on attention augmented convolutional neural network and flow-attention mechanism for identification of anticancer peptides

Zhang,

Zhao,

Liang

2024

Bioinformatics

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Motivation Anticancer peptides (ACPs) have natural cationic properties and can act on the anionic cell membrane of cancer cells to kill cancer cells. Therefore, ACPs have become a potential anticancer drug with good research value and prospect. Results In this paper, we propose AACFlow, an end-to-end model for identification of ACPs based on deep learning. End-to-end models have more room to automatically adjust according to the data, making the overall fit better and reducing error propagation. The combination of attention augmented convolutional neural network (AAConv) and multi-layer convolutional neural network (CNN) forms a deep representation learning module, which is used to obtain global and local information on the sequence. Based on the concept of flow network, multi-head flow-attention mechanism is introduced to mine the deep features of the sequence to improve the efficiency of the model. On the independent test dataset, the ACC, Sn, Sp, and AUC values of AACFlow are 83.9%, 83.0%, 84.8%, and 0.892, respectively, which are 4.9%, 1.5%, 8.0%, and 0.016 higher than those of the baseline model. The MCC value is 67.85%. In addition, we visualize the features extracted by each module to enhance the interpretability of the model. Various experiments show that our model is more competitive in predicting ACPs. Availability The codes and datasets are accessible at https://github.com/z11code/AACFlow. Supplementary information Supplementary data are available at Bioinformatics online.

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Anti-cancer Peptide Recognition Based on Grouped Sequence and Spatial Dimension Integrated Networks

Cited by 8 publications

References 29 publications

ACPPfel: Explainable deep ensemble learning for anticancer peptides prediction based on feature optimization

ACPPfel: Explainable deep ensemble learning for anticancer peptides prediction based on feature optimization

ACP-BC: A Model for Accurate Identification of Anticancer Peptides Based on Fusion Features of Bidirectional Long Short-Term Memory and Chemically Derived Information

AACFlow: an end-to-end model based on attention augmented convolutional neural network and flow-attention mechanism for identification of anticancer peptides

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