YLoc—an interpretable web server for predicting subcellular localization

By Sebastian Briesemeister, Jörg Rahnenführer, Oliver Kohlbacher. 2010

306 citations recorded by the source corpus.

Predicting subcellular localization has become a valuable alternative to time-consuming experimental methods. Major drawbacks of many of these predictors is their lack of interpretability and the fact that they do not provide an estimate of the confidence of an individual prediction. We present YLoc, an interpretable web server for predicting subcellular localization. YLoc uses natural language to explain why a prediction was made and which biological property of the protein was mainly responsible for it. In addition, YLoc estimates the reliability of its own predictions. YLoc can, thus, assist in understanding protein localization and in location engineering of proteins. The YLoc web server is available online at www.multiloc.org/YLoc.

Subfields: Molecular Biology

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Citations in the corpus

Modeling aspects of the language of life through transfer-learning protein sequences (2019, methodology): “Subcellular localization prediction was trained and evaluated using the DeepLoc data set [47] for which performance was measured for several methods, namely: LocTree2 [72], MultiLoc2 [73], Sher-Loc2 [74], CELLO [75], iLoc-Euk [52], WoLF PSORT [76] and YLoc [77].”
COMPARTMENTS: unification and visualization of protein subcellular localization evidence (2014, methodology): “Examples of sequence-based subcellular localization prediction methods are BaCelLo (20), LOCtree2 (21), PSORT (22) and YLoc (23, 24).”
LocTree3 prediction of localization (2014, unknown): “Many machine learning methods predict the native localization of a protein from its amino acid sequence; among the best known are CELLO (7), WoLF PSORT (8), YLoc (9) and PSORTb (10).”
Modeling the language of life – Deep Learning Protein Sequences (2019, methodology): “Localization prediction 3 was trained and evaluated using the DeepLoc data set [46] for which performance was 4 measured for several methods, namely: LocTree2 [66], MultiLoc2 [67], SherLoc2 [68], CELLO 5 [69], iLoc-Euk [51], WoLF PSORT [70] and YLoc [71].”
The in silico human surfaceome (2018, methodology): “Although multiple tools have been published for generalized subcellular localization prediction (12, 13), only two of these are specific for prediction of the subcellular localization of membrane proteins (14, 15).”