ME-ACP: Multi-view neural networks with ensemble model for identification of anticancer peptides

Feng, Guanwen; Yao, Hang; Li, Chaoneng; Liu, Ruyi; Huang, Rungen; Fan, Xiaopeng; Ge, Ruiquan; Miao, Qiguang

doi:10.1016/j.compbiomed.2022.105459

Cited by 11 publications

(9 citation statements)

References 53 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%

“…To evaluate the model, we conducted a systematic evaluation and comparison analysis of the final performance of the model with other related studies. Firstly, we compared our algorithm with the best result of ME-ACP ( Feng et al, 2022 ). As shown in Table 8 , ACPPfel had a 2.63% higher SN on an alternative independent dataset.…”

Section: Discussionmentioning

confidence: 99%

“…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: 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%

Section: Discussionmentioning

confidence: 99%

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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“…In Feng et al (2022) , a novel and highly efficient method, called ME-ACP, is introduced, employing multi-view neural networks combined with an ensemble model for the identification of anticancer peptides. Initially, residue-level and peptide-level features are incorporated using ensemble models based on lightGBMs to yield preliminary results.…”

Section: Related Workmentioning

confidence: 99%

An efficient consolidation of word embedding and deep learning techniques for classifying anticancer peptides: FastText+BiLSTM

Karakaya,

Kilimci

2024

PeerJ Computer Science

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Anticancer peptides (ACPs) are a group of peptides that exhibit antineoplastic properties. The utilization of ACPs in cancer prevention can present a viable substitute for conventional cancer therapeutics, as they possess a higher degree of selectivity and safety. Recent scientific advancements generate an interest in peptide-based therapies which offer the advantage of efficiently treating intended cells without negatively impacting normal cells. However, as the number of peptide sequences continues to increase rapidly, developing a reliable and precise prediction model becomes a challenging task. In this work, our motivation is to advance an efficient model for categorizing anticancer peptides employing the consolidation of word embedding and deep learning models. First, Word2Vec, GloVe, FastText, One-Hot-Encoding approaches are evaluated as embedding techniques for the purpose of extracting peptide sequences. Then, the output of embedding models are fed into deep learning approaches CNN, LSTM, BiLSTM. To demonstrate the contribution of proposed framework, extensive experiments are carried on widely-used datasets in the literature, ACPs250 and independent. Experiment results show the usage of proposed model enhances classification accuracy when compared to the state-of-the-art studies. The proposed combination, FastText+BiLSTM, exhibits 92.50% of accuracy for ACPs250 dataset, and 96.15% of accuracy for the Independent dataset, thence determining new state-of-the-art.

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“…Yuan et al (2023) proposed a novel peptide sequence encoding method called "Ordinal positional encoding" to represent peptides, and combined methods from both deep learning (bi-LSTM [Huang et al, 2015], CNN [LeCun et al, 1998]) and machine learning (LightGBM [Ke et al, 2017]) in their predictor. The ME-ACP proposed by Feng et al (2022) uses multiple lightGBM models to process various features of ACPs into a probability vector. This vector is then input into a hybrid neural network consisting of residual modules and Bi-LSTM module for prediction.…”

Section: Introductionmentioning

confidence: 99%

MA‐PEP: A novel anticancer peptide prediction framework with multimodal feature fusion based on attention mechanism

Liang,

Zhao,

Wang

2024

Protein Science

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AntiCancer Peptides (ACPs) have emerged as promising therapeutic agents for cancer treatment. The time‐consuming and costly nature of wet‐lab discriminatory methods has spurred the development of various machine learning and deep learning‐based ACP classification methods. Nonetheless, current methods encountered challenges in efficiently integrating features from various peptide modalities, thereby limiting a more comprehensive understanding of ACPs and further restricting the improvement of prediction model performance. In this study, we introduce a novel ACP prediction method, MA‐PEP, which leverages multiple attention mechanisms for feature enhancement and fusion to improve ACP prediction. By integrating the enhanced molecular‐level chemical features and sequence information of peptides, MA‐PEP demonstrates superior prediction performance across several benchmark datasets, highlighting its efficacy in ACP prediction. Moreover, the visual analysis and case studies further demonstrate MA‐PEP's reliable feature extraction capability and its promise in the realm of ACP exploration. The code and datasets for MA‐PEP are available at https://github.com/liangxiaodata/MA-PEP.

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ME-ACP: Multi-view neural networks with ensemble model for identification of anticancer peptides

Cited by 11 publications

References 53 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

An efficient consolidation of word embedding and deep learning techniques for classifying anticancer peptides: FastText+BiLSTM

MA‐PEP: A novel anticancer peptide prediction framework with multimodal feature fusion based on attention mechanism

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