Additional file 4 of Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks

Figure S3. The difference of predictive performance using sequence + structure and only sequence. On the y-axis the performance of the full model with sequence + structure is shown. The x-axis shows the performance of the model using only sequences. The two red lines indicate the 2 times standard deviation of the difference between only using sequence and using sequence + structure. (EPS 39 kb

Additional file 1 of Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks

Figure S1. The network architectures of iDeepS. (PNG 45 kb

Additional file 3 of Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks

Table S1. The AUCs of using CNNs with sequence and structure information for different hyperparameters learning rate and weight decay. (PDF 47 kb

Additional file 2 of Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks

Figure S2. The AUCs of using DBN and k-mer features to predict RBP binding sites. (EPS 54.4 KB

Data sources’ characteristics^*.

<p>Data sources’ characteristics^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0160648#t001fn001" target="_blank">*</a>}.</p

Recommended composite algorithms: age band-specific percentages of subjects identified on the relevant total study population.

<p>PPV: Positive Predictive Value.</p

Comparison of results from individual component algorithms: four examples.

<p>Comparison of results from individual component algorithms: four examples.</p

The standard procedure for data derivation.

<p>The standard procedure for data derivation.</p

Impact of extracted component algorithms on total case population identified in each participating data source through the application of the relevant recommended composite algorithm.

<p>Impact of extracted component algorithms on total case population identified in each participating data source through the application of the relevant recommended composite algorithm.</p

Component algorithms description.

<p>Component algorithms description.</p