PHD: predicting one-dimensional protein structure by profile-based neural networks.

By Burkhard Rost. 1996

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Citations in the corpus

NetSurfP-2.0: improved prediction of protein structural features by integrated deep learning (2018, methodology): “As a practical application of it, several methods have been developed over the last decades to predict from sequence only several protein structural features, including solvent accessibility, secondary structure, backbone geometry, disorder, and interface residues (2-7).”
Better prediction of functional effects for sequence variants (2015, neutral): “We tried to avoid local maxima in training via the following steps: S1: Train with balanced data sets [38,40].”
ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Deep Learning and High Performance Computing (2020, background): “New protein representations may complement existing solutions, most successful when combining evolutionary information and machine learning [34], [69], [70], [71].”
A generic method for assignment of reliability scores applied to solvent accessibility predictions (2009, unknown): “By nature, the reliability method is different from other procedures where reliabilities most commonly are obtained by post-processing the output [2,22].”
Natively Unstructured Loops Differ from Other Loops (2007, methodology): “We used a standard feed-forward neural network described elsewhere in more detail [30,32,73,81] The crucial novelty for the given task was the choice of input information.”
Comprehensive in silico mutagenesis highlights functionally important residues in proteins (2008, methodology): “We utilized PROFacc ( Rost, 2000, 2005; Rost and Sander, 1994) to predict location of affected residues in ASEdb in protein structure.”
Predicting protein structure using hidden Markov models (1997, methodology): “We checked PhD secondary structure predictions [20] for consistency with our structure predictions.”
Bioinformatics - a practical guide to the analysis of genes and proteins (2001, background): “Although the online services now provide access to the World Wide Web, most of the specialized features and services available through these systems reside in a proprietary, closed network.”
Cascaded multiple classifiers for secondary structure prediction (2000, unknown): “( Rost, 1996 ). We a nalyzed Prof using this classification.”
Twilight zone of protein sequence alignments. (1999, background): “Sequence space hopping may be used in combination with knowledge from structures to widen families (Holm and Sander, 1997), or to increase the information contained in multiple sequence alignments input to prediction methods (Rost, 1996, 1997).”
TeXshade: shading and labeling of multiple sequence alignments using LaTeX2e (2000, methodology): “Also, PHD files provide topology predictions of membrane proteins indicating Each group of a shading mode is labeled with a different colour.”