Bioinformatics Original Paper Integrating Multi-attribute Similarity Networks for Robust Representation of the Protein Space

By Orhançamog Lu, Tolga Can, Ambuj K. Singh

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Citations in the corpus

SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning (2019, methodology): “JRONN [67], IUPred optimized for short and long disorder [IUpred (short)] and [IUpred (long)] [15], and PONDR-VSL [14] are not discussed here because of lower performance except the second-best shown below.”
Prediction of intrinsic disorder and its use in functional proteomics. (2007, background): “RONN, a regional-order neural network, classifies residues in the space of distances between an input sequence and a set of carefully selected “prototype” sequences (75).”
IPC – Isoelectric Point Calculator (2016, methodology): “Outliers representing possible annotation errors in databases were removed (proteins with mean standard error (MSE) > 3 between the experimental isoelectric point and the average predicted pI; note that under this cutoff, no peptides were removed; it should be stressed that removed outliers do not d”
Bioinformatical approaches to characterize intrinsically disordered/unstructured proteins (2010, methodology): “Using a different approach, RONN predictions [88] are based on similarity to known examples of disordered segments.”
A microscale protein NMR sample screening pipeline (2009, methodology): “2005), Prelink (Coeytaux and Poupon 2005), RONN (Yang et al. 2005), and VSL2 (Peng et al.”
Improved Disorder Prediction by Combination of Orthogonal Approaches (2009, methodology): “Several advanced methods attempt to capture complex relationships between sequence and disorder by using machinelearning algorithms optimized to discriminate between wellstructured and unstructured regions [18,42,43,44,45,46,47]; these methods are usually very good for what they are trained for, for”
Natively Unstructured Loops Differ from Other Loops (2007, result): “Many other studies corroborated the leading role of DISOPRED2 [22,26,41,43,44].”
The RCSB Protein Data Bank: views of structural biology for basic and applied research and education (2014, background): “Alternatively, the annotation has been inferred indirectly, as for Pfam domains (46), or calculated, as for hydropathy scores (47) and propensity for protein disorder score based on JRONN (P. Troshin and G. J. Barton, unpublished), a Java implementation of RONN (48).”