DeepACPpred: A Novel Hybrid CNN-RNN Architecture for Predicting Anti-Cancer Peptides

DeepACPpred: A Novel Hybrid CNN-RNN Architecture for Predicting Anti-Cancer Peptides. Lane, N. & Kahanda, I. In Panuccio, G., Rocha, M., Fdez-Riverola, F., Mohamad, M. S., & Casado-Vara, R., editors, Practical Applications of Computational Biology & Bioinformatics, 14th International Conference (PACBB 2020), pages 60–69, Cham, 2021. Springer International Publishing.
abstract bibtex

Anti-cancer peptides (ACPs) are a promising alternative to traditional chemotherapy. To aid wet-lab and clinical research, there is a growing interest in using machine learning techniques to help identify good ACP candidates computationally. In this paper, we describe DeepACPpred, a novel deep learning model composed of a hybrid CNN-RNN architecture for predicting ACPs. Using several gold-standard ACP datasets, we demonstrate that DeepACPpred is highly effective compared to state-of-the-art ACP prediction models.

@InProceedings{10.1007/978-3-030-54568-0_7,
  author    = {Lane, Nathaniel and Kahanda, Indika},
  booktitle = {Practical Applications of Computational Biology {\&} Bioinformatics, 14th International Conference (PACBB 2020)},
  title     = {DeepACPpred: A Novel Hybrid CNN-RNN Architecture for Predicting Anti-Cancer Peptides},
  editor    = {Panuccio, Gabriella and Rocha, Miguel and Fdez-Riverola, Florentino and Mohamad, Mohd Saberi and Casado-Vara, Roberto},
  isbn      = {978-3-030-54568-0},
  pages     = {60--69},
  publisher = {Springer International Publishing},
  abstract  = {Anti-cancer peptides (ACPs) are a promising alternative to traditional chemotherapy. To aid wet-lab and clinical research, there is a growing interest in using machine learning techniques to help identify good ACP candidates computationally. In this paper, we describe DeepACPpred, a novel deep learning model composed of a hybrid CNN-RNN architecture for predicting ACPs. Using several gold-standard ACP datasets, we demonstrate that DeepACPpred is highly effective compared to state-of-the-art ACP prediction models.},
  address   = {Cham},
  year      = {2021},
}

Downloads: 0

{"_id":"oxWFXTMejHfyQMJwn","bibbaseid":"lane-kahanda-deepacppredanovelhybridcnnrnnarchitectureforpredictinganticancerpeptides-2021","author_short":["Lane, N.","Kahanda, I."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"propositions":[],"lastnames":["Lane"],"firstnames":["Nathaniel"],"suffixes":[]},{"propositions":[],"lastnames":["Kahanda"],"firstnames":["Indika"],"suffixes":[]}],"booktitle":"Practical Applications of Computational Biology & Bioinformatics, 14th International Conference (PACBB 2020)","title":"DeepACPpred: A Novel Hybrid CNN-RNN Architecture for Predicting Anti-Cancer Peptides","editor":[{"propositions":[],"lastnames":["Panuccio"],"firstnames":["Gabriella"],"suffixes":[]},{"propositions":[],"lastnames":["Rocha"],"firstnames":["Miguel"],"suffixes":[]},{"propositions":[],"lastnames":["Fdez-Riverola"],"firstnames":["Florentino"],"suffixes":[]},{"propositions":[],"lastnames":["Mohamad"],"firstnames":["Mohd","Saberi"],"suffixes":[]},{"propositions":[],"lastnames":["Casado-Vara"],"firstnames":["Roberto"],"suffixes":[]}],"isbn":"978-3-030-54568-0","pages":"60–69","publisher":"Springer International Publishing","abstract":"Anti-cancer peptides (ACPs) are a promising alternative to traditional chemotherapy. To aid wet-lab and clinical research, there is a growing interest in using machine learning techniques to help identify good ACP candidates computationally. In this paper, we describe DeepACPpred, a novel deep learning model composed of a hybrid CNN-RNN architecture for predicting ACPs. Using several gold-standard ACP datasets, we demonstrate that DeepACPpred is highly effective compared to state-of-the-art ACP prediction models.","address":"Cham","year":"2021","bibtex":"@InProceedings{10.1007/978-3-030-54568-0_7,\n author = {Lane, Nathaniel and Kahanda, Indika},\n booktitle = {Practical Applications of Computational Biology {\\&} Bioinformatics, 14th International Conference (PACBB 2020)},\n title = {DeepACPpred: A Novel Hybrid CNN-RNN Architecture for Predicting Anti-Cancer Peptides},\n editor = {Panuccio, Gabriella and Rocha, Miguel and Fdez-Riverola, Florentino and Mohamad, Mohd Saberi and Casado-Vara, Roberto},\n isbn = {978-3-030-54568-0},\n pages = {60--69},\n publisher = {Springer International Publishing},\n abstract = {Anti-cancer peptides (ACPs) are a promising alternative to traditional chemotherapy. To aid wet-lab and clinical research, there is a growing interest in using machine learning techniques to help identify good ACP candidates computationally. In this paper, we describe DeepACPpred, a novel deep learning model composed of a hybrid CNN-RNN architecture for predicting ACPs. Using several gold-standard ACP datasets, we demonstrate that DeepACPpred is highly effective compared to state-of-the-art ACP prediction models.},\n address = {Cham},\n year = {2021},\n}\n\n","author_short":["Lane, N.","Kahanda, I."],"editor_short":["Panuccio, G.","Rocha, M.","Fdez-Riverola, F.","Mohamad, M. S.","Casado-Vara, R."],"key":"10.1007/978-3-030-54568-0_7","id":"10.1007/978-3-030-54568-0_7","bibbaseid":"lane-kahanda-deepacppredanovelhybridcnnrnnarchitectureforpredictinganticancerpeptides-2021","role":"author","urls":{},"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"https://bibbase.org/network/files/efHxiSdHcByWYrtAF","dataSources":["sY8SCkRETugqZgTDF"],"keywords":[],"search_terms":["deepacppred","novel","hybrid","cnn","rnn","architecture","predicting","anti","cancer","peptides","lane","kahanda"],"title":"DeepACPpred: A Novel Hybrid CNN-RNN Architecture for Predicting Anti-Cancer Peptides","year":2021}