Identification of cancer-related miRNA-lncRNA biomarkers using a basic miRNA-lncRNA network

The table contains the expression data of miRNAs and lncRNAs in the BRCA, KIRC, LUAD, LUSC, PRAD and THCA

The number of cancer samples remaining after preprocessing.

<p>The number of cancer samples remaining after preprocessing.</p

Identification of cancer-related miRNA-lncRNA biomarkers using a basic miRNA-lncRNA network - Fig 8

<p>(A) A heat map of the top 200 miRNA-lncRNA interactions in the six types of cancers. Each column represents a cancer, and each row represents the significance score of the corresponding miRNA-lncRNA pair. The redder the color, the greater the significance score of the corresponding pair. (B) The distribution of lncRNA biomarkers involved in the top 200 edge biomarkers in distinct cancers.</p

The performance of CPC.

<p>The performance of CPC.</p

LncRNApred: Classification of Long Non-Coding RNAs and Protein-Coding Transcripts by the Ensemble Algorithm with a New Hybrid Feature

<div><p>As a novel class of noncoding RNAs, long noncoding RNAs (lncRNAs) have been verified to be associated with various diseases. As large scale transcripts are generated every year, it is significant to accurately and quickly identify lncRNAs from thousands of assembled transcripts. To accurately discover new lncRNAs, we develop a classification tool of random forest (RF) named LncRNApred based on a new hybrid feature. This hybrid feature set includes three new proposed features, which are MaxORF, RMaxORF and SNR. LncRNApred is effective for classifying lncRNAs and protein coding transcripts accurately and quickly. Moreover,our RF model only requests the training using data on human coding and non-coding transcripts. Other species can also be predicted by using LncRNApred. The result shows that our method is more effective compared with the Coding Potential Calculate (CPC). The web server of LncRNApred is available for free at <a href="http://mm20132014.wicp.net:57203/LncRNApred/home.jsp" target="_blank">http://mm20132014.wicp.net:57203/LncRNApred/home.jsp</a>.</p></div

The classification performance after the pretreatment of clustering.

<p>The classification performance after the pretreatment of clustering.</p

Boxplots of the top 13 features: MaxORF, RMaxORF, SNR, Length, CG%, CGG%, GC%, CCG%, GCG%, CGC%, GCC%, G% and (G+C)%.

<p>For each plot, the left side represents the mRNA, and the right side represents lncRNA.</p

The result of SOM clustering.

<p>The left side represents the distribution in the 64 neurons of lncRNAs. Every digit of the hexagon is the number of lncRNAs which belong to one class. The right side represents the distribution in the 64 neurons of mRNAs, and every digit of hexagon is the number of mRNAs which belong to one class.</p

The AUC of distinct features of node biomarkers and edge biomarkers.

<p>The AUC of distinct features of node biomarkers and edge biomarkers.</p

Two dimensional SOM neural network model.

<p>Two dimensional SOM neural network model.</p