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

Nathaniel Lane; Indika Kahanda

doi:10.1007/978-3-030-54568-0_7

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

Montana State University

Research output: Chapter or Contribution to Book › Literary contribution

Original language	English
Title of host publication	Practical Applications of Computational Biology and Bioinformatics, 14th International Conference, PACBB 2020
Editors	Gabriella Panuccio, Miguel Rocha, Florentino Fdez-Riverola, Mohd Saberi Mohamad, Roberto Casado-Vara
Pages	60-69
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-030-54568-0_7
State	Published - 2021
Externally published	Yes
Event	14th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2020 - L´Aquila, Italy Duration: Jun 17 2020 → Jun 19 2020

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1240 AISC
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	14th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2020
Country/Territory	Italy
City	L´Aquila
Period	6/17/20 → 6/19/20

ASJC Scopus Subject Areas

Control and Systems Engineering
General Computer Science

Keywords

Anti-cancer Peptides
Convolutional Neural Networks
Recurrent Neural Networks

Access to Document

10.1007/978-3-030-54568-0_7

Cite this

Lane, N., & Kahanda, I. (2021). DeepACPpred: A Novel Hybrid CNN-RNN Architecture for Predicting Anti-Cancer Peptides. In G. Panuccio, M. Rocha, F. Fdez-Riverola, M. S. Mohamad, & R. Casado-Vara (Eds.), Practical Applications of Computational Biology and Bioinformatics, 14th International Conference, PACBB 2020 (pp. 60-69). (Advances in Intelligent Systems and Computing; Vol. 1240 AISC). https://doi.org/10.1007/978-3-030-54568-0_7

DeepACPpred: A Novel Hybrid CNN-RNN Architecture for Predicting Anti-Cancer Peptides. / Lane, Nathaniel; Kahanda, Indika.
Practical Applications of Computational Biology and Bioinformatics, 14th International Conference, PACBB 2020. ed. / Gabriella Panuccio; Miguel Rocha; Florentino Fdez-Riverola; Mohd Saberi Mohamad; Roberto Casado-Vara. 2021. p. 60-69 (Advances in Intelligent Systems and Computing; Vol. 1240 AISC).

Research output: Chapter or Contribution to Book › Literary contribution

Lane, N & Kahanda, I 2021, DeepACPpred: A Novel Hybrid CNN-RNN Architecture for Predicting Anti-Cancer Peptides. in G Panuccio, M Rocha, F Fdez-Riverola, MS Mohamad & R Casado-Vara (eds), Practical Applications of Computational Biology and Bioinformatics, 14th International Conference, PACBB 2020. Advances in Intelligent Systems and Computing, vol. 1240 AISC, pp. 60-69, 14th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2020, L´Aquila, Italy, 6/17/20. https://doi.org/10.1007/978-3-030-54568-0_7

Lane N, Kahanda I. DeepACPpred: A Novel Hybrid CNN-RNN Architecture for Predicting Anti-Cancer Peptides. In Panuccio G, Rocha M, Fdez-Riverola F, Mohamad MS, Casado-Vara R, editors, Practical Applications of Computational Biology and Bioinformatics, 14th International Conference, PACBB 2020. 2021. p. 60-69. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-54568-0_7

Lane, Nathaniel ; Kahanda, Indika. / DeepACPpred : A Novel Hybrid CNN-RNN Architecture for Predicting Anti-Cancer Peptides. Practical Applications of Computational Biology and Bioinformatics, 14th International Conference, PACBB 2020. editor / Gabriella Panuccio ; Miguel Rocha ; Florentino Fdez-Riverola ; Mohd Saberi Mohamad ; Roberto Casado-Vara. 2021. pp. 60-69 (Advances in Intelligent Systems and Computing).

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author = "Nathaniel Lane and Indika Kahanda",

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

Publication series

Conference

ASJC Scopus Subject Areas

Keywords

Access to Document

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