ME-ACP: Multi-view Neural Networks with Ensemble Model for Identification of Anticancer Peptides Article Swipe
Guanwen Feng
,
Hang Yao
,
Chaoneng Li
,
Ruyi Liu
,
Rungen Huang
,
Xiaopeng Fan
,
Ruiquan Ge
,
Qiguang Miao
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.11.22.469543
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.11.22.469543
Cancer remains one of the most threatening diseases, which kills millions of lives every year. As a promising perspective for cancer treatments, anticancer peptides (ACPs) overcome a lot of disadvantages of traditional treatments. However, it is time-consuming and expensive to identify ACPs through conventional experiments. Hence, it is urgent and necessary to develop highly effective approaches to accurately identify ACPs in large amounts of protein sequences. In this work, we proposed a novel and effective method named ME-ACP which employed multi-view neural networks with ensemble model to identify ACPs. Firstly, we employed residue level and peptide level features preliminarily with ensemble models based on lightGBMs. Then, the outputs of lightGBM classifiers were fed into a hybrid deep neural network (HDNN) to identify ACPs. The experiments on independent test datasets demonstrated that ME-ACP achieved competitive performance on common evaluation metrics.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2021.11.22.469543
- https://www.biorxiv.org/content/biorxiv/early/2021/11/23/2021.11.22.469543.full.pdf
- OA Status
- green
- Cited By
- 2
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3215825649
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Raw OpenAlex JSON
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https://doi.org/10.1101/2021.11.22.469543Digital Object Identifier
- Title
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ME-ACP: Multi-view Neural Networks with Ensemble Model for Identification of Anticancer PeptidesWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-11-22Full publication date if available
- Authors
-
Guanwen Feng, Hang Yao, Chaoneng Li, Ruyi Liu, Rungen Huang, Xiaopeng Fan, Ruiquan Ge, Qiguang MiaoList of authors in order
- Landing page
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https://doi.org/10.1101/2021.11.22.469543Publisher landing page
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https://www.biorxiv.org/content/biorxiv/early/2021/11/23/2021.11.22.469543.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2021/11/23/2021.11.22.469543.full.pdfDirect OA link when available
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Artificial neural network, Computer science, Identification (biology), Artificial intelligence, Machine learning, Ensemble forecasting, Data mining, Biology, BotanyTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2024: 1, 2023: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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