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Computational Prediction of RNA Binding Proteins: Features and Models

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Title Computational Prediction of RNA Binding Proteins: Features and Models
Not Available
 
Creator Upendra Kumar Pradhan
Prabina Kumar Meher
 
Subject RNA binding proteins
Machine learning
Models
features
 
Description Not Available
To date, two major layers of gene regulation have been identified during RNA life cycle. These include: transcription regulation by transcription and epigenetic factors and post-transcription regulation by various classes of small RNA (sRNAs). Besides these two layers, there is another layer of gene regulation which is completely regulated by RNA-binding proteins (RBPs). RBPs play crucial roles in gene expression and regulation in both transcriptional and post-transcriptional levels. RBPs account for approximately 6–8% of all proteins. A key objective of computational biology is to identify these RBPs. Although a variety of experimental methods for RBP identification have been developed, these techniques are costly, time-consuming, and labour-intensive. Alternatively, researchers have developed multiple computational approaches for predicting RBPs by integrating multiple machine learning and deep learning methods with numerical RBP features. In this chapter, we discuss the computational methods, datasets, and features for computational recognition of RBPs. We believe that this chapter will provide valuable information as far as computational prediction of RBPs is concerned.
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Date 2023-12-20T10:27:14Z
2023-12-20T10:27:14Z
2023-12-02
 
Type Book chapter
 
Identifier Pradhan UK,Meher PK (2023). Computational Prediction of RNA Binding Proteins: Features and Models. In Bioinformatics and Computational Biology. Chapman and Hall/CRC. https://doi.org/10.1201/9781003331247-14
9781003331247
http://krishi.icar.gov.in/jspui/handle/123456789/81080
 
Language English
 
Relation Not Available;
 
Publisher Chapman and Hall/CRC