2019
DOI: 10.1093/aobpla/plz068
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Abstract: The subcellular localization of proteins is very important for characterizing its function in a cell. Accurate prediction of the subcellular locations in computational paradigm has been an active area of interest. Most of the work has been focused on single localization prediction. Only few studies have discussed the multi-target localization, but have not achieved good accuracy so far; in plant sciences, very limited work has been done. Here we report the development of a novel tool Plant-mSubP, which is base… Show more

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Cited by 53 publications
(47 citation statements)
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“…A training and testing dataset obtained from Plant-mSubP [ 11 ] was used to train and evaluate the performance of the program for 11 protein locations. These data were already filtered according to the criterion of <30% similarity, using the BLASTclust, as described in [ 11 ].…”
Section: Methodsmentioning
confidence: 99%
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“…A training and testing dataset obtained from Plant-mSubP [ 11 ] was used to train and evaluate the performance of the program for 11 protein locations. These data were already filtered according to the criterion of <30% similarity, using the BLASTclust, as described in [ 11 ].…”
Section: Methodsmentioning
confidence: 99%
“…A training and testing dataset obtained from Plant-mSubP [ 11 ] was used to train and evaluate the performance of the program for 11 protein locations. These data were already filtered according to the criterion of <30% similarity, using the BLASTclust, as described in [ 11 ]. In this work, to reduce the high imbalance observed for plastids and cell membranes versus the remaining locations, we applied CD-HIT [ 36 ], with a similarity cutoff of <25%, followed by random subsampling for the proteins in the plastid and cell membrane locations.…”
Section: Methodsmentioning
confidence: 99%
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“…Cellular localization was predicted using the plant subcellular location predictor (http://bioinfo.usu.edu/Plant-mSubP/). 39 …”
Section: Methodsmentioning
confidence: 99%