IIMLP: integrated information-entropy-based method for LncRNA prediction

Abstract Background The prediction of long non-coding RNA (lncRNA) has attracted great attention from researchers, as more and evidence indicate that various complex human diseases are closely related to lncRNAs. In the era bio-med big data, in addition lncRNAs by biological experimental methods, many computational methods based on machine learning have been proposed make better use sequence resources Results We developed lncRNA method integrating information-entropy-based features algorithms. calculate generalized topological entropy generate 6 novel for sequences. By employing these other such open reading frame, we apply supporting vector machine, XGBoost random forest algorithms distinguish compare our with one which K-mer results show higher area under curve up 99.7905%. Conclusions develop an accurate efficient information analyze classify Our is also extendable research functional elements DNA