Gene Ontology-based function prediction of long non-coding RNAs using bi-random walk

Zhang, Jingpu; Zou, Shuai; Deng, Lei

doi:10.1186/s12920-018-0414-2

Cited by 24 publications

(9 citation statements)

References 45 publications

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“…The performance of FGGA-lnc classifiers was compared against three established methods in the literature: the LncRNA2Function [31], the BiRWLGO [34] and the Chen method [48]. For this purpose, precision, recall and F -score performance metrics were computed.…”

Section: Resultsmentioning

confidence: 99%

“…One step further, the outstanding ability of lncRNAs to interact with a wide spectrum of biomolecules can be exploited. In this regard, computational methods based on interaction networks, co-expression and associations between lncRNAs and proteins [30][31][32][33][34][35] have emerged in recent years. Although these methods can be very accurate, they require a large amount of experimental data that is available only for exhaustively studied organisms like Homo sapiens and Mus musculus.…”

Section: Introductionmentioning

confidence: 99%

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FGGA-lnc: automatic gene ontology annotation of lncRNA sequences based on secondary structures

Spetale

Murillo

Villanova³

et al. 2021

Interface Focus.

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The study of long non-coding RNAs (lncRNAs), greater than 200 nucleotides, is central to understanding the development and progression of many complex diseases. Unlike proteins, the functionality of lncRNAs is only subtly encoded in their primary sequence. Current in-silico lncRNA annotation methods mostly rely on annotations inferred from interaction networks. But extensive experimental studies are required to build these networks. In this work, we present a graph-based machine learning method called FGGA-lnc for the automatic gene ontology (GO) annotation of lncRNAs across the three GO subdomains. We build upon FGGA (factor graph GO annotation), a computational method originally developed to annotate protein sequences from non-model organisms. In the FGGA-lnc version, a coding-based approach is introduced to fuse primary sequence and secondary structure information of lncRNA molecules. As a result, lncRNA sequences become sequences of a higher-order alphabet allowing supervised learning methods to assess individual GO-term annotations. Raw GO annotations obtained in this way are unaware of the GO structure and therefore likely to be inconsistent with it. The message-passing algorithm embodied by factor graph models overcomes this problem. Evaluations of the FGGA-lnc method on lncRNA data, from model and non-model organisms, showed promising results suggesting it as a candidate to satisfy the huge demand for functional annotations arising from high-throughput sequencing technologies.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

FGGA-lnc: automatic gene ontology annotation of lncRNA sequences based on secondary structures

Spetale

Murillo

Villanova³

et al. 2021

Interface Focus.

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“…Then, we carried out the random walk method for both the lncRNA similarity network and the disease similarity network, called bi-random walk, a global process ( Zhang et al, 2018 ). r 1 and r 2 are designated as the maximum number of iterations in the lncRNA and disease similarity networks, respectively.…”

Section: Methodsmentioning

confidence: 99%

NCP-BiRW: A Hybrid Approach for Predicting Long Noncoding RNA-Disease Associations by Network Consistency Projection and Bi-Random Walk

et al. 2022

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Long non-coding RNAs (lncRNAs) play significant roles in the disease process. Understanding the pathological mechanisms of lncRNAs during the course of various diseases will help clinicians prevent and treat diseases. With the emergence of high-throughput techniques, many biological experiments have been developed to study lncRNA-disease associations. Because experimental methods are costly, slow, and laborious, a growing number of computational models have emerged. Here, we present a new approach using network consistency projection and bi-random walk (NCP-BiRW) to infer hidden lncRNA-disease associations. First, integrated similarity networks for lncRNAs and diseases were constructed by merging similarity information. Subsequently, network consistency projection was applied to calculate space projection scores for lncRNAs and diseases, which were then introduced into a bi-random walk method for association prediction. To test model performance, we employed 5- and 10-fold cross-validation, with the area under the receiver operating characteristic curve as the evaluation indicator. The computational results showed that our method outperformed the other five advanced algorithms. In addition, the novel method was applied to another dataset in the Mammalian ncRNA-Disease Repository (MNDR) database and showed excellent performance. Finally, case studies were carried out on atherosclerosis and leukemia to confirm the effectiveness of our method in practice. In conclusion, we could infer lncRNA-disease associations using the NCP-BiRW model, which may benefit biomedical studies in the future.

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“…In other words, these solutions ignore the different contributions of different nodes in the network. Some variant solutions prefix two or three walk-lengths in different subnetworks [24], [33], but these variants still do not assign individual walk-lengths to different nodes. Given that, we advocate to perform asynchronous random walk with each node having its own walk-length to infer the associations between proteins and GO terms.…”

Section: Asynchronous Random Walk For Protein Function Predictionmentioning

confidence: 99%

Cross-Species Protein Function Prediction with Asynchronous-Random Walk

Zhao

Wang

Guo

et al. 2021

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Protein function prediction is a fundamental task in the postgenomic era. Available functional annotations of proteins are incomplete and the annotations of two homologous species are complementary to each other. However, how to effectively leverage mutually complementary annotations of different species to further boost the prediction performance is still not well studied. In this paper, we propose a cross-species protein function prediction approach by performing Asynchronous Random Walk on a heterogeneous network (AsyRW). AsyRW firstly constructs a heterogeneous network to integrate multiple functional association networks derived from different biological data, established homology-relationships between proteins from different species, known annotations of proteins and Gene Ontology (GO). To account for the intrinsic structures of intra-and inter-species of proteins and that of GO, AsyRW quantifies the individual walk lengths of each network node using the gravity-like theory and performs asynchronousrandom walk with the individual length to predict associations between proteins and GO terms. Experiments on annotations archived in different years show that individual walk length and asynchronous-random walk can effectively leverage the complementary annotations of different species, AsyRW has a significantly improved performance to other related and competitive methods. The codes of AsyRW are available at: http://mlda.swu.edu.cn/codes.php?name=AsyRW.

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Gene Ontology-based function prediction of long non-coding RNAs using bi-random walk

Cited by 24 publications

References 45 publications

FGGA-lnc: automatic gene ontology annotation of lncRNA sequences based on secondary structures

FGGA-lnc: automatic gene ontology annotation of lncRNA sequences based on secondary structures

NCP-BiRW: A Hybrid Approach for Predicting Long Noncoding RNA-Disease Associations by Network Consistency Projection and Bi-Random Walk

Cross-Species Protein Function Prediction with Asynchronous-Random Walk

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