TopologyNet: Topology based deep convolutional neural networks for biomolecular property predictions

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 entangled geometric complexity and biological complexity. We introduce topology, i.e., element specific persistent homology (ESPH), to untangle geometric complexity and biological complexity. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains crucial biological information via a multichannel image representation. It is able to reveal hidden structure-function relationships in biomolecules. We further integrate ESPH and convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the limitations to deep learning arising from small and noisy training sets, we present a multitask topological convolutional neural network (MT-TCNN). We demonstrate that the present TopologyNet architectures outperform other state-of-the-art methods in the predictions of protein-ligand binding affinities, globular protein mutation impacts, and membrane protein mutation impacts.Comment: 20 pages, 8 figures, 5 table

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筑波大学修士(情報学)学位論文・平成31年3月25日授与(41289号

Application and future trends of spinal cord stimulation

Neuropathic pain impacts 7-10% of the general population and seriously undermines quality of life despite available medications. Although initially approved to treat chronic neuropathic pain as an alternative to conventional medical management, spinal cord stimulation (SCS) is expanding its application prospect to the treatment for an assortment of indications including ischemic pain and neurodegenerative disorders, with new stimulation modalities, techniques, and electrode materials emerging every year. Despite its proven efficacy and cost-effectiveness when compared with the long-term application of insufficiently effective and potentially harmful medications, SCS is still largely neglected by pain physicians and neurosurgeons worldwide because of the exorbitant cost of the devices and possible complications. The mechanism of action, constituents and clinical applications, and performance of SCS are here reviewed, with a special focus on five indications amenable to SCS treatment, including failed back surgery syndrome (FBSS), complex regional pain syndrome (CRPS), painful diabetic neuropathy (PDN), critical limb ischemia (CLI) and Parkinson’s disease (PD). Among all the indications, only FBSS and CRPS have a mature application scenario, and SCS treatment for PDN has just recently been approved by FDA. The clinical study of more conditions that may benefit from SCS treatment, such as CLI and PD, is still underway. Market expectations and recent developments of SCS are further discussed to provide an outlook for the future trends of spinal cord stimulation