Mooney, CatherineCatherineMooneyWang, Yong-HongYong-HongWangPollastri, GianlucaGianlucaPollastri2021-05-282021-05-282011 Sprin2010-09-18978-3-642-21945-0http://hdl.handle.net/10197/12222The 7th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2010), Palermo, Italy, 16-18 September 2010Knowledge 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.enThe final publication is available at www.springerlink.com.Support vector machineSecretory pathwayChloroplast transit peptideWoLF PSORTBinary SVMsConference Publication10.1007/978-3-642-21946-7_32021-05-2510/RFP/GEN274905/RFP/CMS0029https://creativecommons.org/licenses/by-nc-nd/3.0/ie/