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      SignalP 5.0 improves signal peptide predictions using deep neural networks

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          Abstract

          Signal peptides (SPs) are short amino acid sequences in the amino terminus of many newly synthesized proteins that target proteins into, or across, membranes. Bioinformatic tools can predict SPs from amino acid sequences, but most cannot distinguish between various types of signal peptides. We present a deep neural network-based approach that improves SP prediction across all domains of life and distinguishes between three types of prokaryotic SPs.

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          Most cited references 41

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          Deep learning.

          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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            Long Short-Term Memory

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              SignalP 4.0: discriminating signal peptides from transmembrane regions.

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                Author and article information

                Journal
                Nature Biotechnology
                Nat Biotechnol
                Springer Nature
                1087-0156
                1546-1696
                February 18 2019
                Article
                10.1038/s41587-019-0036-z
                30778233
                © 2019

                http://www.springer.com/tdm

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