Long non-coding RNAs (lncRNAs) are a class of RNAs with the length exceeding 200 base pairs (bps), which do not encode proteins, nevertheless, lncRNAs have many vital biological functions. A large number of novel transcripts were discovered as a result of the development of high-throughput sequencing technology. Under this circumstance, computational methods for lncRNA prediction are in great demand. In this paper, we consider global sequence features and propose a stacked ensemble learning-based method to predict lncRNAs from transcripts, abbreviated as PredLnc-GFStack. We extract the critical features from the candidate feature list using the genetic algorithm (GA) and then employ the stacked ensemble learning method to construct PredLnc-GFStack model. Computational experimental results show that PredLnc-GFStack outperforms several state-of-the-art methods for lncRNA prediction. Furthermore, PredLnc-GFStack demonstrates an outstanding ability for cross-species ncRNA prediction.

Enhancer-promoter interactions (EPIs) regulate the expression of specific genes in cells, which help facilitate understanding of gene regulation, cell differentiation and disease mechanisms. EPI identification approaches through wet experiments are often costly and time-consuming, leading to the design of high-efficiency computational methods is in demand. In this paper, we propose a deep neural network-based method named EPIHC to predict Enhancer-Promoter Interactions with Hybrid features and Communicative learning. EPIHC extracts enhancer and promoter sequence-derived features using convolutional neural networks (CNN), and then we design a communicative learning module to capture the communicative information between enhancer and promoter sequences. Besides, EPIHC takes the genomic features of enhancers and promoters into account, incorporating with the sequence-derived features to predict EPIs. The computational experiments show that EPIHC outperforms the existing state-of-the-art EPI prediction methods on the benchmark datasets and chromosome-split datasets, and the study reveals that the communicative learning module can bring explicit information about EPIs, which is ignored by CNN, and provide explainability about EPIs to some degree. Moreover, we consider two strategies to improve the performances of EPIHC in the cross-cell line prediction, and experimental results show that EPIHC constructed on some cell lines can exhibit good performances for other cell lines. The codes and data are available at https://github.com/BioMedicalBigDataMiningLab/EPIHC.

Background: 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.Results: 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.Conclusions: LncPred-IEL and LncPred-ANEL are promising lncRNA prediction tools that can effectively utilize and integrate the information in different types of features.