TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions.

ABSTRACT: Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH) method. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-

SUBMITTER: Cang Z 

PROVIDER: S-EPMC5549771 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature