Structured Sparse Regularization based Random Vector Functional Link Networks for DNA N4-methylcytosine sites prediction

Ding, Yijie (författare): University Of Electronic Science And Technology Of China, Chengdu, China

Qian, Yuqing (författare): University Of Electronic Science And Technology Of China, Chengdu, China; University Of Electronic Science And Technology Of China, Chengdu, China

Tiwari, Prayag, 1991- (författare): Högskolan i Halmstad,Akademin för informationsteknologi

(creator_code:org_t)

Oxford : Elsevier, 2024
2024
Engelska.
Ingår i: Expert systems with applications. - Oxford : Elsevier. - 0957-4174 .- 1873-6793. ; 235

Relaterad länk:: https://doi.org/10.1...; visa fler...; https://urn.kb.se/re...; https://doi.org/10.1...; visa färre...

Abstract Ämnesord

Stäng

As an epigenetic modification that plays an important role in modifying gene function and controlling gene expression during cell development, DNA N4-methylcytosine (4mC) is still lack of researching. It is therefore necessary to accurately predict the 4mC sites to make fully aware of its mechanism and function. In this paper, we propose a novel model which is called Structural Sparse Regularized Random Vector Functional Link Network (SSR-RVFL) for predicting 4mC sites. Compared with other state-of-the-art methods, SSR-RVFL performs better and achieves higher prediction accuracy. There are total six benchmark datasets used in the experiments, namely C.elegans, D.elanogaster, E.coli, A.thaliana G.subterraneus and G.pickeringii. Our model improves the accuracy by 0.42%, 0.45%, 0.48%, 0.91%, 0.66% and 0.7% on these six benchmark datasets respectively, so it can be regarded as a more effective prediction tool. © 2023 The Author(s)

Av författaren/redakt...: Xie, Hao; Ding, Yijie; Qian, Yuqing; Tiwari, Prayag, ...; Guo, Fei

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