PTPD : Predicting therapeutic peptides by deep learning and word2vec
(2019) In BMC Bioinformatics 20(1).- Abstract
Background In the search for therapeutic peptides for disease treatments, many efforts have been made to identify various functional peptides from large numbers of peptide sequence databases. In this paper, we propose an effective computational model that uses deep learning and word2vec to predict therapeutic peptides (PTPD).∗: Results Representation vectors of all k-mers were obtained through word2vec based on k-mer co-existence information. The original peptide sequences were then divided into k-mers using the windowing method. The peptide sequences were mapped to the input layer by the embedding vector obtained by word2vec. Three types of filters in the convolutional layers, as well as dropout and max-pooling operations, were applied... (More)
Background In the search for therapeutic peptides for disease treatments, many efforts have been made to identify various functional peptides from large numbers of peptide sequence databases. In this paper, we propose an effective computational model that uses deep learning and word2vec to predict therapeutic peptides (PTPD).∗: Results Representation vectors of all k-mers were obtained through word2vec based on k-mer co-existence information. The original peptide sequences were then divided into k-mers using the windowing method. The peptide sequences were mapped to the input layer by the embedding vector obtained by word2vec. Three types of filters in the convolutional layers, as well as dropout and max-pooling operations, were applied to construct feature maps. These feature maps were concatenated into a fully connected dense layer, and rectified linear units (ReLU) and dropout operations were included to avoid over-fitting of PTPD. The classification probabilities were generated by a sigmoid function. PTPD was then validated using two datasets: an independent anticancer peptide dataset and a virulent protein dataset, on which it achieved accuracies of 96% and 94%, respectively.∗: Conclusions PTPD identified novel therapeutic peptides efficiently, and it is suitable for application as a useful tool in therapeutic peptide design.
(Less)
- author
- Wu, Chuanyan LU ; Gao, Rui ; Zhang, Yusen and De Marinis, Yang LU
- organization
- publishing date
- 2019-09-06
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Deep learning, Therapeutic peptide, Word2vec
- in
- BMC Bioinformatics
- volume
- 20
- issue
- 1
- article number
- 456
- publisher
- BioMed Central (BMC)
- external identifiers
-
- pmid:31492094
- scopus:85071896624
- ISSN
- 1471-2105
- DOI
- 10.1186/s12859-019-3006-z
- language
- English
- LU publication?
- yes
- id
- f9daa232-30df-43bf-8ed9-aea3d584f3ba
- date added to LUP
- 2019-09-16 14:34:43
- date last changed
- 2024-04-16 20:13:52
@article{f9daa232-30df-43bf-8ed9-aea3d584f3ba,
abstract = {{<p>Background In the search for therapeutic peptides for disease treatments, many efforts have been made to identify various functional peptides from large numbers of peptide sequence databases. In this paper, we propose an effective computational model that uses deep learning and word2vec to predict therapeutic peptides (PTPD).∗: Results Representation vectors of all k-mers were obtained through word2vec based on k-mer co-existence information. The original peptide sequences were then divided into k-mers using the windowing method. The peptide sequences were mapped to the input layer by the embedding vector obtained by word2vec. Three types of filters in the convolutional layers, as well as dropout and max-pooling operations, were applied to construct feature maps. These feature maps were concatenated into a fully connected dense layer, and rectified linear units (ReLU) and dropout operations were included to avoid over-fitting of PTPD. The classification probabilities were generated by a sigmoid function. PTPD was then validated using two datasets: an independent anticancer peptide dataset and a virulent protein dataset, on which it achieved accuracies of 96% and 94%, respectively.∗: Conclusions PTPD identified novel therapeutic peptides efficiently, and it is suitable for application as a useful tool in therapeutic peptide design.</p>}},
author = {{Wu, Chuanyan and Gao, Rui and Zhang, Yusen and De Marinis, Yang}},
issn = {{1471-2105}},
keywords = {{Deep learning; Therapeutic peptide; Word2vec}},
language = {{eng}},
month = {{09}},
number = {{1}},
publisher = {{BioMed Central (BMC)}},
series = {{BMC Bioinformatics}},
title = {{PTPD : Predicting therapeutic peptides by deep learning and word2vec}},
url = {{http://dx.doi.org/10.1186/s12859-019-3006-z}},
doi = {{10.1186/s12859-019-3006-z}},
volume = {{20}},
year = {{2019}},
}