Prediction of Human LncRNAs Based on Integrated Information Entropy Features

The prediction of long non-coding RNA (lncRNA) has attracted great attention from researchers, as more and more evidence indicate that various complex human diseases are closely related to lncRNAs. In the era of bio-big data, in addition to the prediction of lncRNAs by biological experimental methods, many computational methods based on machine learning have been proposed to make better use of the sequence resources of existing long non-coding RNAs. We use a lncRNA prediction method by integrating information-entropy-based features and machine learning algorithms. We calculate generalized topological entropy and generate 6 novel features for lncRNA sequences. By employing these 6 features and other features such as open reading frame (ORF), we apply Supporting Vector Machine (SVM), XGBoost and Random Forest (RF) algorithms to distinguish human lncRNAs. We compare our method with the one which has more Kmer features and results show that our method has higher Area Under the Curve (AUC) up to 99.7905%. We develop an accurate and efficient method which has novel information entropy features to analyze and classify lncRNAs. Our method is also extendable for research on other functional elements in DNA sequences.