SubCons: a new ensemble method for improved human subcellular localization predictions | Zendy

Marco Salvatore | Zendy; Per Warholm | Zendy; Nanjiang Shu | Zendy; Walter Basile | Zendy; Arne Elofsson | Zendy

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SubCons: a new ensemble method for improved human subcellular localization predictions

Author(s) -

Marco Salvatore,

Per Warholm,

Nanjiang Shu,

Walter Basile,

Arne Elofsson

Publication year - 2017

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btx219

Subject(s) - computer science , classifier (uml) , subcellular localization , source code , artificial intelligence , code (set theory) , web server , random forest , data mining , machine learning , the internet , biology , cytoplasm , set (abstract data type) , biochemistry , world wide web , programming language , operating system

Knowledge of the correct protein subcellular localization is necessary for understanding the function of a protein. Unfortunately large-scale experimental studies are limited in their accuracy. Therefore, the development of prediction methods has been limited by the amount of accurate experimental data. However, recently large-scale experimental studies have provided new data that can be used to evaluate the accuracy of subcellular predictions in human cells. Using this data we examined the performance of state of the art methods and developed SubCons, an ensemble method that combines four predictors using a Random Forest classifier.

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