EDLMFC: an ensemble deep learning framework with multi-scale features combination for ncRNA-protein interaction prediction.

ABSTRACT:

Background

Non-coding RNA (ncRNA) and protein interactions play essential roles in various physiological and pathological processes. The experimental methods used for predicting ncRNA-protein interactions are time-consuming and labor-intensive. Therefore, there is an increasing demand for computational methods to accurately and efficiently predict ncRNA-protein interactions.

Results

In this work, we presented an ensemble deep learning-based method, EDLMFC, to predict ncRNA-protein interactions using the combination of multi-scale features, including primary sequence features, secondary structure sequence features, and tertiary structure features. Conjoint k-mer was used to extract protein/ncRNA sequence features, integrating tertiary structure features, then fed into an ense

SUBMITTER: Wang J 

PROVIDER: S-EPMC7980572 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature