LncPredIEL: A Long Noncoding RNA Prediction Method using Iterative Ensemble Learning A large number of transcripts have been generated by the development of high throughput sequencing technologies. Predicting lncRNA from transcripts is a challenging and important task. In this paper, we propose LncPredIEL, an iterative ensemble learning long noncoding RNA prediction method. LncPredIEL not only considers features widely used for the lncRNA prediction, but also take into account sequencederived features used in the RNA sequence classification, so as to make use of diverse information. LncPredIEL builds base predictors based on different groups of features, and employs a supervised iterative way to combine base predictors and build ensemble models. Our studies demonstrate that supervised iterative way can learn the representations that help to separate lncRNA and proteincoding transcripts, and further improve the performances. Experiments demonstrate that LncPredIEL outperforms several stateoftheart methods when evaluated by 10fold crossvalidation. The capability of LncPredIEL for the crossspecies prediction is also tested. As complementary to wet experiments, LncPredIEL is a useful computational tool for lncRNA prediction.

LncRNAmiRNA interaction prediction from the heterogeneous network through graph embedding ensemble learning LncRNAmiRNA interactions play crucial roles in gene regulatory networks and can reveal functions of lncRNAs and miRNAs. Although several methods have been proposed to infer interactions on the lncRNAmiRNA interaction network, few attentions have been paid to fully exploiting the structure of lncRNAmiRNA interaction network. In this paper, we propose a Graph Embedding Ensemble Learning method (abbreviated as xe2x80x9cGEELxe2x80x9d) to predict lncRNAmiRNA interactions. First, we collect lncRNA sequences and miRNA sequences to calculate lncRNAlncRNA sequence similarity and miRNAmiRNA sequence similarity, and then we combine them with the known lncRNAmiRNA interactions to construct a heterogeneous network, which takes lncRNAs and miRNAs as nodes. We adopt graph embedding methods to learn representations of lncRNAs and miRNAs from the heterogeneous network, and then merge the representations of lncRNAs and miRNAs to represent the lncRNAmiRNA pairs. Random forest classifiers are built based on the merged representations to predict lncRNAmiRNA interactions. We consider five different graph embedding methods, and evaluate the corresponding models. Further, we use individual graph embedding methodbased model as base predictors and build a highlevel ensemble model. The experimental results show that GEEL achieves AUPR score of 0.7004 and AUC score of 0.9537, and outperforms base predictors and other stateoftheart methods. In conclusion, GEEL is an effective tool for lncRNAmiRNA interaction prediction.