Transfer Learning Allows Accurate RBP Target Site Prediction with Limited Sample Sizes

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F23%3A00131807" target="_blank" >RIV/00216224:14310/23:00131807 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2079-7737/12/10/1276" target="_blank" >https://www.mdpi.com/2079-7737/12/10/1276</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/biology12101276" target="_blank" >10.3390/biology12101276</a>

Result language

angličtina

Original language name

Transfer Learning Allows Accurate RBP Target Site Prediction with Limited Sample Sizes

Original language description

RNA-binding proteins are vital regulators in numerous biological processes. Their disfunction can result in diverse diseases, such as cancer or neurodegenerative disorders, making the prediction of their binding sites of high importance. Deep learning (DL) has brought about a revolution in various biological domains, including the field of protein–RNA interactions. Nonetheless, several challenges persist, such as the limited availability of experimentally validated binding sites to train well-performing DL models for the majority of proteins. Here, we present a novel training approach based on transfer learning (TL) to address the issue of limited data. Employing a sophisticated and interpretable architecture, we compare the performance of our method trained using two distinct approaches: training from scratch (SCR) and utilizing TL. Additionally, we benchmark our results against the current state-of-the-art methods. Furthermore, we tackle the challenges associated with selecting appropriate input features and determining optimal interval sizes. Our results show that TL enhances model performance, particularly in datasets with minimal training data, where satisfactory results can be achieved with just a few hundred RNA binding sites. Moreover, we demonstrate that integrating both sequence and evolutionary conservation information leads to superior performance. Additionally, we showcase how incorporating an attention layer into the model facilitates the interpretation of predictions within a biologically relevant context.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EF18_053%2F0016952" target="_blank" >EF18_053/0016952: Postdoc2MUNI</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Biology

ISSN

2079-7737

e-ISSN

2079-7737

Volume of the periodical

12

Issue of the periodical within the volume

10

Country of publishing house

CH - SWITZERLAND

Number of pages

19

Pages from-to

1-19

UT code for WoS article

001090019100001

EID of the result in the Scopus database

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