CRlncRC: a machine learning-based method for cancer-related long noncoding RNA identification using integrated features

Abstract

BackgroundLong noncoding RNAs (lncRNAs) are widely involved in the initiation and development of cancer. Although some computational methods have been proposed to identify cancer-related lncRNAs, there is still a demanding to improve the prediction accuracy and efficiency. In addition, the quick-update data of cancer, as well as the discovery of new mechanism, also underlay the possibility of improvement of cancer-related lncRNA prediction algorithm. In this study, we introduced CRlncRC, a novel Cancer-Related lncRNA Classifier by integrating manifold features with five machine-learning techniques.ResultsCRlncRC was built on the integration of genomic, expression, epigenetic and network, totally in four categories of features. Five learning techniques were exploited to develop the effective classification model including Random Forest (RF), Naïve bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR) and K-Nearest Neighbors (KNN). Using ten-fold cross-validation, we showed that RF is the best model for classifying cancer-related lncRNAs (AUC = 0.82). The feature importance analysis indicated that epigenetic and network features play key roles in the classification. In addition, compared with other existing classifiers, CRlncRC exhibited a better performance both in sensitivity and specificity. We further applied CRlncRC to lncRNAs from the TANRIC (The Atlas of non-coding RNA in Cancer) dataset, and identified 121 cancer-related lncRNA candidates. These potential cancer-related lncRNAs showed a certain kind of cancer-related indications, and many of them could find convincing literature supports.ConclusionsOur results indicate that CRlncRC is a powerful method for identifying cancer-related lncRNAs. Machine-learning-based integration of multiple features, especially epigenetic and network features, had a great contribution to the cancer-related lncRNA prediction. RF outperforms other learning techniques on measurement of model sensitivity and specificity. In addition, using CRlncRC method, we predicted a set of cancer-related lncRNAs, all of which displayed a strong relevance to cancer as a valuable conception for the further cancer-related lncRNA function studies.Electronic supplementary materialThe online version of this article (10.1186/s12920-018-0436-9) contains supplementary material, which is available to authorized users.

A systematic review of the application of machine learning in the detection and classification of transposable elements

“…The advantages of using ML in bioinformatics include the discovery of entirely new information such as arrays of mobile genetic elements, new transposition unit boundaries (Tsafnat et al, 2011), and predicting new long noncoding RNA that are related to cancer (Zhang et al, 2018). Other applications include extracting discriminatory features for automatically determining functional properties of biological sequences (Kamath, De Jong & Shehu, 2014), identifying DNA motifs, which is a difficult task in non-ML applications (Dashti & Masoudi-Nejad, 2010), and automating specific processes like the identification of long non-coding RNAs (Ventola et al, 2017) and the classification of LTR retrotransposons (Arango-López et al, 2017).…”

“…Several articles using ML or DL techniques reported that TEs are associated with many human diseases (Zhang et al, 2013). For example, cancer-related long noncoding RNAs have higher SINE and LINE numbers than cancer-unrelated long noncoding RNAs (Zhang et al, 2018). Likewise, several types of epithelial cancers acquire somatic insertions of LINE-1 as they develop (mentioned in Tang et al (2017)).…”

Peer Review #1 of "A systematic review of the application of machine learning in the detection and classification of transposable elements (v0.1)"

“…Several papers using ML or DL techniques reported that TEs are associated with many human diseases (Zhang et al, 2013). For example, cancer-related long noncoding RNAs have higher SINE and LINE numbers than cancer-unrelated long noncoding RNAs (Zhang et al, 2018). Likewise, several types of epithelial cancers acquire somatic insertions of LINE-1 as they develop (mentioned in (Tang et al, 2017)).…”

“…Long non-coding RNAs (lncRNAs) are gaining attention because of critical biological functions suggested by recent studies (for a review see (Mercer, Dinger & Mattick, 2009)). Some of the ML applications found included the detection of cancer-related lncRNA (Zhang et al, 2018), the discrimination of circular RNAs from other lncRNAs (Chen et al, 2018), and selection of the most informative features of lncRNA (Ventola et al, 2017). Other applications in the RNA field address the identification and clustering of RNA structure motifs (Smith et al, 2017),…”

“…PRISMA flow diagram Publication identifier Year Q1 Q2 Q3 Q4 Reference Publication identifier Year Q1 Q2 Q3 Q4 Reference P1 X X X X (Yu, Yu & Pan, 2017) P19 2013 X X X (Loureiro et al, 2013b) P2 X X X (Schietgat et al, 2018) P20 2014 X X (Ma, Zhang & Wang, 2014) P3 X X X (Arango-López et al, 2017) P21 2010 X X (Dashti & Masoudi-Nejad, 2010) P4 X X X (Loureiro et al, 2013a) P22 2010 X X (Ding, Zhou & Guan, 2010) P5 X X X (Tsafnat et al, 2011) P23 2019 X (Jaiswal & Krishnamachari, 2019) P6 X X X (Zhang et al, 2018) P24 2015 X X X X (Girgis, 2015) P7 X X X (Eraslan et al, 2019) P25 2018 X X X (Nakano et al, 2018a) P8 X X X (Douville et al, 2018) P26 2018 X X X (Zamith Santos et al, 2018) P9 X X (Chen et al, 2018) P27 2009 X (Abrusan et al, 2009) P10 X X X X (Ashlock & Datta, 2012) P28 2019 X X (Su, Gu & Peterson, 2019) P11 X X X (Smith et al, 2017) P29 2017 X X X X (Nakano et al, 2017) P12 X X X X (Kamath, De Jong & Shehu, 2014) P30 2014 X X X (Brayet et al, 2014) P13 X X X (Kim et al, 2016) P31 2013 X (Zamani et al, 2013) P14 X X X (Segal et al, 2018) P32 2019 X (Hubbard et al, 2019) P15 X X X (Rawal & Ramaswamy, 2011) P33 2014 X X (Ryvkin et al, 2014) P16 X X X (Tang et al, 2017) P34 2013 X X X X (Zhang et al, 2013) P17 X X X (Ventola et al, 2017) P35 2019 X X…”