PROSITE: a dictionary of sites and patterns in proteins.

By Amos Bairoch. 1991

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Citations in the corpus

MRFalign: Protein Homology Detection through Alignment of Markov Random Fields (2014, background): “Sequence signature libraries [14] and intermediate sequence based methods [15,16] are also developed to make use of evolutionary information of a protein.”
Understanding eukaryotic linear motifs and their role in cell signaling and regulation. (2008, background): “ch/prosite/) (96) made the first systematic attempt to catalogue known motifs.”
Position-based sequence weights. (1994, methodology): “As then, we used the PROSITE catalog of protein families ( Bairoch, 1992 ) to provide lists of true positives for assessing performance.”
Homology Detection via Family Pairwise Search (1998, unknown): “These families were selected from the Prosite database (Bairoch, 1992) for their difficulty, based upon the number of false positives reported in the Prosite annotations.”
The Spectrum Kernel: A String Kernel for SVM Protein Classification (2001, methodology): “Many approaches have been presented for the protein classi(cid:12)cation problem, including methods based on pairwise similarity of sequences 1 ; 2 ; 3 , pro(cid:12)les for protein families 4 , consensus patterns using motifs 5 ; 6 and hidden Markov models 7 ; 8 ; 9 .”
Flexible sequence similarity searching with the FASTA3 program package. (2000, background): “Motif databases, such as PROSITE (21), can provide evidence for the conservation of critical functional residues.”
PANTHER: a library of protein families and subfamilies indexed by function. (2003, background): “…and pattern-matching approaches (Jongeneel et al. 1989) on sequence databases led to the development of databases of profiles (BLOCKS, Henikoff and Henikoff 1991; PRINTS, Attwood et al. 1994) and patterns (Prosite, Bairoch 1991) that could be searched in much the same way as sequence databases.”
Adaptive computation and machine learning (1998, methodology): “A promising approach to improve the sensitivity and speed of current database-searching techniques has been to use consensus models built from multiple alignments of protein families [23, 52, 250, 334, 41, 38].”
A motif-based profile scanning approach for genome-wide prediction of signaling pathways (2001, methodology): “nificantly better than that found using a strict pattern recognition approach 21 .”
Hidden Markov models in computational biology. Applications to protein modeling. (1993, methodology): “Similarly, if an HMM is built for a protein domain or motif, then it can be used to search for occurrences of this domain or motif in the database, much like a PROSITE expression (Bairoch, 1992), a commonly used method for searching for patterns found in protein sequences.”