{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T16:53:32Z","timestamp":1768928012690,"version":"3.49.0"},"reference-count":17,"publisher":"Springer Science and Business Media LLC","issue":"S3","license":[{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T00:00:00Z","timestamp":1594252800000},"content-version":"vor","delay-in-days":8,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2020,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n<jats:title>Background<\/jats:title>\n<jats:p>Circular RNAs (circRNAs) are those RNA molecules that lack the poly (A) tails, which present the\u00a0closed-loop structure. Recent studies emphasized that some\u00a0circRNAs imply different functions from canonical transcripts, and further associated with complex diseases. Several\u00a0computational methods have been developed for detecting circRNAs from RNA-seq data. However, the existing methods prefer to high sensitivity strategies, which always\u00a0introduce many false positives. Thus, in clinical decision-supporting system, a comprehensive filtering approach is needed for accurately recognizing real\u00a0circRNAs\u00a0for decision models.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Methods<\/jats:title>\n<jats:p>In this paper, we first reviewed the detection strategies of the existing methods. According to the features from RNA-seq\u00a0data, we showed that\u00a0any single feature (data signal) selected by the existing strategies cannot accurately distinguish a\u00a0circRNA. However, we found that some combinations of those\u00a0features (data signals) could be used as signatures\u00a0for recognizing circRNAs. To avoid the\u00a0high computational complexity of the combinational optimization problem, we present CIRCPlus2, which adopts a machine learning framework to recognize real\u00a0circRNAs according to multiple data signals captured from RNA-seq data. By comparing multiple machine learning frameworks, CIRCPlus2 adopts a Gradient Boosting Decision Tree (GBDT) framework.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Results<\/jats:title>\n<jats:p>Given a set of candidate circRNAs, reported by any existing detection tool(s), the features of each candidate are extracted from the aligned reads. The GBDT framework can be\u00a0trained by a\u00a0training dataset. By applying the selected\u00a0features on the framework, the predictions on true\/false positives are reported. To verify the performance\u00a0of the proposed approach, we conducted several groups of experiments on both real RNA-seq datasets and a series of simulation datasets with different preset configurations. The results demonstrated that CIRCPlus2 clearly improved the specificities, while it also maintained high levels of sensitivities.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Conclusions<\/jats:title>\n<jats:p>Filtering false positives is quite important in RNA-seq data analysis pipeline. Machine learning framework is suitable for solving this\u00a0filtering problem. CIRCPlus2 is an efficient approach to identify the false positive\u00a0circRNAs from the real ones.<\/jats:p>\n<\/jats:sec>","DOI":"10.1186\/s12911-020-1117-0","type":"journal-article","created":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T08:08:50Z","timestamp":1594282130000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A machine learning framework for accurately recognizing circular RNAs for clinical decision-supporting"],"prefix":"10.1186","volume":"20","author":[{"given":"Yidan","family":"Wang","sequence":"first","affiliation":[]},{"given":"Xuanping","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Tao","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jinchun","family":"Xing","sequence":"additional","affiliation":[]},{"given":"Zhun","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jiayin","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,7,9]]},"reference":[{"key":"1117_CR1","doi-asserted-by":"publisher","first-page":"792","DOI":"10.1016\/j.molcel.2013.08.017","volume":"51","author":"Y Zhang","year":"2013","unstructured":"Zhang Y, Zhang X, Chen T, Xiang J, Yin Q, Xing Y, et al. 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