Wyszukiwarka
Abstrakt
RNA protein interactions (RPI) play a pivotal role in the regulation of various biological processes. Experimental validation of RPI has been time-consuming, paving the way for computational prediction methods. The major limiting factor of these methods has been the accuracy and confidence of the predictions, and our in-house experiments show that they fail to accurately predict RPI involving short RNA sequences such as TERRA RNA. Here, we present a data-driven model for RPI prediction using a gradient boosting classifier. Amino acids and nucleotides are classified based on the high-resolution structural data of RNA protein complexes. The minimum structural unit consisting of five residues is used as the descriptor. Comparative analysis of existing methods shows the consistently higher performance of our method irrespective of the length of RNA present in the RPI. The method has been successfully applied to map RPI networks involving both long noncoding RNA as well as TERRA RNA. The method is also shown to successfully predict RNA and protein hubs present in RPI networks of four different organisms. The robustness of this method will provide a way for predicting RPI networks of yet unknown interactions for both long noncoding RNA and microRNA.
Cytowania
-
1 5
CrossRef
-
0
Web of Science
-
1 2
Scopus
Autorzy (3)
-
Dharm Skandh Jain
-
Sanket Rajan Gupte
-
Raviprasad Aduri
Cytuj jako
Pełna treść
- Wersja publikacji
- Accepted albo Published Version
- Licencja
-
otwiera się w nowej karcie
Słowa kluczowe
Informacje szczegółowe
- Kategoria:
- Publikacja w czasopiśmie
- Typ:
- artykuły w czasopismach
- Opublikowano w:
-
Scientific Reports
nr 8,
strony 1 - 10,
ISSN: 2045-2322 - Język:
- angielski
- Rok wydania:
- 2018
- Opis bibliograficzny:
- Jain D., Gupte S., Aduri R.: A Data Driven Model for Predicting RNA-Protein Interactions based on Gradient Boosting Machine// Scientific Reports -Vol. 8,iss. 1 (2018), s.1-10
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1038/s41598-018-27814-2
- Weryfikacja:
- Politechnika Gdańska
wyświetlono 119 razy
Publikacje, które mogą cię zainteresować
Explainable machine learning for diffraction patterns
- S. Nawaz,
- V. Rahmani,
- D. Pennicard
- + 3 autorów
Machine Learning and Deep Learning Methods for Fast and Accurate Assessment of Transthoracic Echocardiogram Image Quality
- W. Nazar,
- K. Nazar,
- L. Daniłowicz-Szymanowicz
MP3vec: A Reusable Machine-Constructed Feature Representation for Protein Sequences
- S. R. Gupte,
- D. S. Jain,
- A. Srinivasan
- + 1 autorów