Identification of plant microRNAs using convolutional neural network

被引：0

作者：

Zhang, Yun ^{[1
]}

Huang, Jianghua ^{[1
]}

Xie, Feixiang ^{[1
]}

Huang, Qian ^{[1
]}

Jiao, Hongguan ^{[1
]}

Cheng, Wenbo ^{[1
]}

机构：

[1] Guizhou Univ Tradit Chinese Med, Coll Informat Engn, Guiyang, Guizhou, Peoples R China

来源：

FRONTIERS IN PLANT SCIENCE | 2024年 / 15卷

基金：

中国国家自然科学基金;

关键词：

deep learning; plant; microRNA; !text type='Java']Java[!/text; SRICATs; ANNOTATION; TOOL; CRITERIA;

D O I：

10.3389/fpls.2024.1330854

中图分类号：

Q94 [植物学];

学科分类号：

071001 ;

摘要：

MicroRNAs (miRNAs) are of significance in tuning and buffering gene expression. Despite abundant analysis tools that have been developed in the last two decades, plant miRNA identification from next-generation sequencing (NGS) data remains challenging. Here, we show that we can train a convolutional neural network to accurately identify plant miRNAs from NGS data. Based on our methods, we also present a user-friendly pure Java-based software package called Small RNA-related Intelligent and Convenient Analysis Tools (SRICATs). SRICATs encompasses all the necessary steps for plant miRNA analysis. Our results indicate that SRICATs outperforms currently popular software tools on the test data from five plant species. For non-commercial users, SRICATs is freely available at https://sourceforge.net/projects/sricats.

引用

下载

页数：9

共 50 条

[41] Identification of Indian butterflies using Deep Convolutional Neural Network
Theivaprakasham, Hari
JOURNAL OF ASIA-PACIFIC ENTOMOLOGY, 2021, 24 (01) : 329 - 340
[42] Road Sign Identification with Convolutional Neural Network Using TensorFlow
Kherarba, Mohammed
Abbes, Mounir Tahar
Boumerdassi, Selma
Meddah, Mohammed
Benhamada, Abdelhak
Senouci, Mohammed
MACHINE LEARNING FOR NETWORKING, MLN 2020, 2021, 12629 : 255 - 264
[43] Identification of Defects in Casting Products by using a Convolutional Neural Network
Ekambaram D.
Ponnusamy V.
IEIE Transactions on Smart Processing and Computing, 2022, 11 (03): : 149 - 155
[44] Identification of Chili Plant Diseases Based on Leaves Using Hyperparameter Optimization Architecture Convolutional Neural Network
Murinto
Winiarti, Sri
Pujiyanta, Ardi
International Journal of Advanced Computer Science and Applications, 2024, 15 (11) : 859 - 865
[45] PLANT SPECIES CLASSIFICATION USING HYPERSPECTRAL LIDAR WITH CONVOLUTIONAL NEURAL NETWORK
Tian, Wenxin
Tang, Lingli
Chen, Yuwei
Li, Ziyang
Qiu, Shi
Li, Xiaohui
Zhu, Jiajia
Jiang, Changhui
Hu, Peilun
Jia, Jianxin
Wu, Haohao
Chen, Linsheng
Hyyppa, Juha
2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022), 2022, : 1740 - 1743
[46] Banana Plant Disease Classification Using Hybrid Convolutional Neural Network
Narayanan, K. Lakshmi
Krishnan, R. Santhana
Robinson, Y. Harold
Julie, E. Golden
Vimal, S.
Saravanan, V.
Kaliappan, M.
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2022, 2022
[47] Paddy Plant Disease Classification and Prediction Using Convolutional Neural Network
Sagarika, G. K.
Prasad, Krishna S. J.
Kumar, Mohana S.
2020 5TH IEEE INTERNATIONAL CONFERENCE ON RECENT TRENDS ON ELECTRONICS, INFORMATION, COMMUNICATION & TECHNOLOGY (RTEICT-2020), 2020, : 208 - 214
[48] Intelligent plant disease diagnosis using convolutional neural network: a review
Joseph, Diana Susan
Pawar, Pranav M.
Pramanik, Rahul
MULTIMEDIA TOOLS AND APPLICATIONS, 2023, 82 (14) : 21415 - 21481
[49] Intelligent plant disease diagnosis using convolutional neural network: a review
Diana Susan Joseph
Pranav M Pawar
Rahul Pramanik
Multimedia Tools and Applications, 2023, 82 : 21415 - 21481
[50] Optimized convolutional neural network model for plant species identification from leaf images using computer vision
Reddy, Satti R. G.
Varma, G. P. Saradhi
Davuluri, Rajya Lakshmi
INTERNATIONAL JOURNAL OF SPEECH TECHNOLOGY, 2021, 26 (1) : 23 - 50

← 1 2 3 4 5 →