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 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

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

Cymbidium nipponicum Makino

原著和名: マヤラン科名: ラン科 = Orchidaceae採集地: 鹿児島県 種子島 生姜山 (大隅 種子島 生姜山)採集日: 1970/8/4採集者: 萩庭丈壽整理番号: JH015338国立科学博物館整理番号: TNS-VS-96533