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A deep learning approach for orphan gene identification in moso bamboo (Phyllostachys edulis) based on the CNN + Transformer model

BMC bioinformatics, 2022-05, Vol.23 (1), p.162-162, Article 162 [Peer Reviewed Journal]

2022. The Author(s). ;COPYRIGHT 2022 BioMed Central Ltd. ;2022. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2022 ;ISSN: 1471-2105 ;EISSN: 1471-2105 ;DOI: 10.1186/s12859-022-04702-1 ;PMID: 35513802

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Title:
A deep learning approach for orphan gene identification in moso bamboo (Phyllostachys edulis) based on the CNN + Transformer model
Author: Zhang, Xiaodan ; Xuan, Jinxiang ; Yao, Chensong ; Gao, Qijuan ; Wang, Lianglong ; Jin, Xiu ; Li, Shaowen
Subjects: Amino acids ; Artificial neural networks ; Bamboo ; Computer applications ; Convolutional neural network ; Correlation coefficient ; Correlation coefficients ; Deep learning ; Environmental stress ; Flowers & plants ; Gene expression ; Genes ; Genetic aspects ; Genomes ; Geometric accuracy ; Machine learning ; Moso bamboo ; Natural language processing ; Neural networks ; Orphan genes ; Phyllostachys edulis ; Proteins ; Recurrent neural networks ; Short term memory ; Stresses ; Transformer neural network ; Transformers
Is Part Of: BMC bioinformatics, 2022-05, Vol.23 (1), p.162-162, Article 162
Description: Orphan gene play an important role in the environmental stresses of many species and their identification is a critical step to understand biological functions. Moso bamboo has high ecological, economic and cultural value. Studies have shown that the growth of moso bamboo is influenced by various stresses. Several traditional methods are time-consuming and inefficient. Hence, the development of efficient and high-accuracy computational methods for predicting orphan genes is of great significance. In this paper, we propose a novel deep learning model (CNN + Transformer) for identifying orphan genes in moso bamboo. It uses a convolutional neural network in combination with a transformer neural network to capture k-mer amino acids and features between k-mer amino acids in protein sequences. The experimental results show that the average balance accuracy value of CNN + Transformer on moso bamboo dataset can reach 0.875, and the average Matthews Correlation Coefficient (MCC) value can reach 0.471. For the same testing set, the Balance Accuracy (BA), Geometric Mean (GM), Bookmaker Informedness (BM), and MCC values of the recurrent neural network, long short-term memory, gated recurrent unit, and transformer models are all lower than those of CNN + Transformer, which indicated that the model has the extensive ability for OG identification in moso bamboo. CNN + Transformer model is feasible and obtains the credible predictive results. It may also provide valuable references for other related research. As our knowledge, this is the first model to adopt the deep learning techniques for identifying orphan genes in plants.
Publisher: England: BioMed Central Ltd
Language: English
Identifier: ISSN: 1471-2105
EISSN: 1471-2105
DOI: 10.1186/s12859-022-04702-1
PMID: 35513802
Source: GFMER Free Medical Journals
PubMed Central
Directory of Open Access Journals
ROAD: Directory of Open Access Scholarly Resources
ProQuest Central
Springer Nature OA Free Journals

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A deep learning approach for orphan gene identification in moso bamboo (Phyllostachys edulis) based on the CNN + Transformer model

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