Searching journal content for articles similar to Kelley et al..

...a reliable and quantitative understanding of the alterations of genomic structures under different cellular conditions. The genomic structure yielded by CTG serves as an architectural blueprint of the dynamic gene regulatory network, based on which cell-specific correspondence between gene...

...species should generalize to closely matched cell types in a related species. To assess the viability of cross-species TF binding prediction, we train neural networks to discriminate ChIP-seq peak locations from genomic background and evaluate their performance within and across species. Cross...

.... 2015 first showed that convolutional neural networks (CNNs) can learn TF/RBP binding sites with high accuracy compared to state-of-the-art methods, using only the DNA/RNA sequences as input. Since then, several convolutional and recurrent neural network models for genomics data have improved prediction...

.... Recently, deep neural networks were also applied to discriminate between 147-bp-long sequences bound by a nucleosome and 147-bp-long sequences without any nucleosomes (Di Gangi et al. 2018; Zhang et al. 2018).Building on these previous works, we use here convolutional neural networks (CNNs) to predict...

.... In this study, we describe our first place solution to the 2017 ENCODE-DREAM in vivo TF binding site prediction challenge. By carefully addressing multisource biases and information imbalance across cell types, we created a pipeline that significantly outperforms the current state-of-the-art methods...

...operator (Fig. 1C). By scanning across all 10,240 positions, the convolutional layer can capture the upstream and downstream information, and key determinants of TF binding will trigger an activation, such as a motif match and open chromatin (see Methods, “The Neural Network Architecture of Leopard...