MFPINC: prediction of plant ncRNAs based on multi-source feature fusion.
BMC Genomics
; 25(1): 531, 2024 May 30.
Article
in En
| MEDLINE
| ID: mdl-38816689
ABSTRACT
Non-coding RNAs (ncRNAs) are recognized as pivotal players in the regulation of essential physiological processes such as nutrient homeostasis, development, and stress responses in plants. Common methods for predicting ncRNAs are susceptible to significant effects of experimental conditions and computational methods, resulting in the need for significant investment of time and resources. Therefore, we constructed an ncRNA predictor(MFPINC), to predict potential ncRNA in plants which is based on the PINC tool proposed by our previous studies. Specifically, sequence features were carefully refined using variance thresholding and F-test methods, while deep features were extracted and feature fusion were performed by applying the GRU model. The comprehensive evaluation of multiple standard datasets shows that MFPINC not only achieves more comprehensive and accurate identification of gene sequences, but also significantly improves the expressive and generalization performance of the model, and MFPINC significantly outperforms the existing competing methods in ncRNA identification. In addition, it is worth mentioning that our tool can also be found on Github ( https//github.com/Zhenj-Nie/MFPINC ) the data and source code can also be downloaded for free.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
RNA, Plant
/
Computational Biology
/
RNA, Untranslated
Language:
En
Journal:
BMC Genomics
Journal subject:
GENETICA
Year:
2024
Document type:
Article
Affiliation country:
China