De Novo Protein Subcellular Localization Prediction by N-to-1 Neural Networks
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|Title:||De Novo Protein Subcellular Localization Prediction by N-to-1 Neural Networks||Authors:||Mooney, Catherine; Wang, Yong-Hong; Pollastri, Gianluca||Permanent link:||http://hdl.handle.net/10197/12222||Date:||18-Sep-2010||Online since:||2021-05-28T15:05:19Z||Abstract:||Knowledge of the subcellular location of a protein provides valuable information about its function and possible interaction with other proteins. In the post-genomic era, fast and accurate predictors of subcellular location are required if this abundance of sequence data is to be fully exploited. We have developed a subcellular localization predictor (SCL pred) which predicts the location of a protein into four classes for animals and fungi and five classes for plants (secretory pathway, cytoplasm, nucleus, mitochondrion and chloroplast) using high throughput machine learning techniques trained on large non-redundant sets of protein sequences. The algorithm powering SCL pred is a novel Neural Network (N-to-1 Neural Network, or N1-NN) which is capable of mapping whole sequences into single properties (a functional class, in this work) without resorting to predefined transformations, but rather by adaptively compressing the sequence into a hidden feature vector. We benchmark SCL pred against other publicly available predictors using two benchmarks including a new subset of Swiss-Prot release 57. We show that SCL pred compares favourably to the other state-of-the-art predictors. Moreover, the N1-NN algorithm is fully general and may be applied to a host of problems of similar shape, that is, in which a whole sequence needs to be mapped into a fixed-size array of properties, and the adaptive compression it operates may even shed light on the space of protein sequences.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Publisher:||Springer||Series/Report no.:||Lecture Notes in Computer Science; 6685||Copyright (published version):||2011 Springer||Keywords:||Support vector machine; Secretory pathway; Chloroplast transit peptide; WoLF PSORT; Binary SVMs||DOI:||10.1007/978-3-642-21946-7_3||Other versions:||http://cibb10.pa.icar.cnr.it/||Language:||en||Status of Item:||Peer reviewed||Is part of:||Rizzo, R. and Lisboa, P. J. G. (eds.). Computational Intelligence Methods for Bioinformatics and Biostatistics: 7th International Meeting, CIBB 2010, Palermo, Italy, September 16-18, 2010, Revised Selected Papers||Conference Details:||The 7th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2010), Palermo, Italy, 16-18 September 2010||ISBN:||978-3-642-21945-0||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||Computer Science Research Collection|
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