Predicting Long non-coding RNAs through feature ensemble learning

• Published in: BMC genomics 2020-12, Vol.21 (Suppl 13), p.865-865, Article 865
• Main Authors: Xu, Yanzhen, Zhao, Xiaohan, Liu, Shuai, Zhang, Wen
• Format: Article
• Language: English
• Subjects: Analysis; Attention mechanism; Computational Biology; Computer applications; Datasets; Deep learning; Ensemble learning; Feature ensemble learning; Gene expression; Genetic transcription; Genomics; Identification and classification; Information processing; Iterative methods; lncRNA prediction; Machine Learning; Neural networks; Non-coding RNA; Predictions; Research methodology; RNA; RNA sequencing; RNA, Long Noncoding - genetics; State-of-the-art reviews; Structure; Support vector machines; Transcription
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/s12864-020-07237-y

Many transcripts have been generated due to the development of sequencing technologies, and lncRNA is an important type of transcript. Predicting lncRNAs from transcripts is a challenging and important task. Traditional experimental lncRNA prediction methods are time-consuming and labor-intensive. Efficient computational methods for lncRNA prediction are in demand. In this paper, we propose two lncRNA prediction methods based on feature ensemble learning strategies named LncPred-IEL and LncPred-ANEL. Specifically, we encode sequences into six different types of features including transcript-specified features and general sequence-derived features. Then we consider two feature ensemble strategies to utilize and integrate the information in different feature types, the iterative ensemble learning (IEL) and the attention network ensemble learning (ANEL). IEL employs a supervised iterative way to ensemble base predictors built on six different types of features. ANEL introduces an attention mechanism-based deep learning model to ensemble features by adaptively learning the weight of individual feature types. Experiments demonstrate that both LncPred-IEL and LncPred-ANEL can effectively separate lncRNAs and other transcripts in feature space. Moreover, comparison experiments demonstrate that LncPred-IEL and LncPred-ANEL outperform several state-of-the-art methods when evaluated by 5-fold cross-validation. Both methods have good performances in cross-species lncRNA prediction. LncPred-IEL and LncPred-ANEL are promising lncRNA prediction tools that can effectively utilize and integrate the information in different types of features.