Results 1 - 10 of 1,317

SCOP: a structural classification of proteins database for the investigation of sequences and structures.

by Tim J P Hubbard , Bart Ailey , Steven E Brenner , Alexey G Murzin , Cyrus Chothia - J. Mol. Biol. , 1995
"... ABSTRACT The Structural Classification of Proteins (SCOP) database provides a detailed and comprehensive description of the relationships of all known proteins structures. The classification is on hierarchical levels: the first two levels, family and superfamily, describe near and far evolutionary ..."
Abstract - Cited by 1552 (24 self) - Add to MetaCart
ABSTRACT The Structural Classification of Proteins (SCOP) database provides a detailed and comprehensive description of the relationships of all known proteins structures. The classification is on hierarchical levels: the first two levels, family and superfamily, describe near and far evolutionary

CATH -- a hierarchic classification of protein domain structures

by C A Orengo, A D Michie, S Jones, D T Jones, M B Swindells, J M Thornton - STRUCTURE , 1997
"... Background: Protein evolution gives rise to families of structurally related proteins, within which sequence identities can be extremely low. As a result, structure-based classifications can be effective at identifying unanticipated relationships in known structures and in optimal cases function can ..."
Abstract - Cited by 470 (33 self) - Add to MetaCart
Background: Protein evolution gives rise to families of structurally related proteins, within which sequence identities can be extremely low. As a result, structure-based classifications can be effective at identifying unanticipated relationships in known structures and in optimal cases function

A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features

by Scott Cost, Steven Salzberg - Machine Learning , 1993
"... In the past, nearest neighbor algorithms for learning from examples have worked best in domains in which all features had numeric values. In such domains, the examples can be treated as points and distance metrics can use standard definitions. In symbolic domains, a more sophisticated treatment of t ..."
Abstract - Cited by 309 (3 self) - Add to MetaCart
space. We show that this technique produces excellent classification accuracy on three problems that have been studied by machine learning researchers: predicting protein secondary structure, identifying DNA promoter sequences, and pronouncing English text. Direct experimental comparisons with the other

Multi-label classification: An overview

by Grigorios Tsoumakas, Ioannis Katakis - Int J Data Warehousing and Mining , 2007
"... Nowadays, multi-label classification methods are increasingly required by modern applications, such as protein function classification, music categorization and semantic scene classification. This paper introduces the task of multi-label classification, organizes the sparse related literature into a ..."
Abstract - Cited by 229 (10 self) - Add to MetaCart
into a structured presentation and performs comparative experimental results of certain multi-label classification methods. It also contributes the definition of concepts for the quantification of the multi-label nature of a data set.

The Quadratic Eigenvalue Problem

by Françoise Tisseur, Karl Meerbergen , 2001
"... . We survey the quadratic eigenvalue problem, treating its many applications, its mathematical properties, and a variety of numerical solution techniques. Emphasis is given to exploiting both the structure of the matrices in the problem (dense, sparse, real, complex, Hermitian, skew-Hermitian) and t ..."
Abstract - Cited by 260 (21 self) - Add to MetaCart
. We survey the quadratic eigenvalue problem, treating its many applications, its mathematical properties, and a variety of numerical solution techniques. Emphasis is given to exploiting both the structure of the matrices in the problem (dense, sparse, real, complex, Hermitian, skew

Mismatch string kernels for discriminative protein classification

by Christina S. Leslie, Eleazar Eskin, Adiel Cohen, Jason Weston, William Stafford Noble - Bioinformatics , 2004
"... Motivation: Classification of proteins sequences into functional and structural families based on sequence homology is a central problem in computational biology. Discriminative supervised machine learning approaches provide good performance, but simplicity and computational efficiency of training a ..."
Abstract - Cited by 193 (9 self) - Add to MetaCart
Motivation: Classification of proteins sequences into functional and structural families based on sequence homology is a central problem in computational biology. Discriminative supervised machine learning approaches provide good performance, but simplicity and computational efficiency of training

Sequence Comparisons Using Multiple Sequences Detect Three Times as Many Remote . . .

by Jong Park, Kevin Karplus, Christian Barrett, Richard Hughey, David Haussler, Tim Hubbard, Cyrus Chothia , 1998
"... The sequences of related proteins can diverge beyond the point where their relationship can be recognised by pairwise sequence comparisons. In attempts to overcome this limitation, methods have been developed that use as a query, not a single sequence, but sets of related sequences or a representati ..."
Abstract - Cited by 244 (16 self) - Add to MetaCart
detect evolutionary relationships between the members of the sequence database PDBD40-J. This database, derived from the structural classification of proteins (SCOP), contains the sequences of proteins of known structure whose sequence identities with each other are 40 % or less. The evolutionary

Touring protein fold space with DALI/FSSP

by Liisa Holm, Chris S - Nucleic Acids Res , 1998
"... The FSSP database and its new supplement, the Dali Domain Dictionary, present a continuously updated classification of all known 3D protein structures. The classification is derived using an automatic structure alignment program (Dali) for the all-against-all comparison of structures in the Protein ..."
Abstract - Cited by 188 (0 self) - Add to MetaCart
The FSSP database and its new supplement, the Dali Domain Dictionary, present a continuously updated classification of all known 3D protein structures. The classification is derived using an automatic structure alignment program (Dali) for the all-against-all comparison of structures in the Protein

SCOP: a Structural Classification of Proteins database

by Loredana Lo Conte, Bart Ailey, Tim J. P. Hubbard, Alexey G. Murzin, Cyrus Chothia , 2000
"... The Structural Classification of Proteins (SCOP) database provides a detailed and comprehensive description of the relationships of known protein structures. The classification is on hierarchical levels: the first two levels, family and superfamily, describe near and distant evolutionary relationshi ..."
Abstract - Cited by 124 (0 self) - Add to MetaCart
The Structural Classification of Proteins (SCOP) database provides a detailed and comprehensive description of the relationships of known protein structures. The classification is on hierarchical levels: the first two levels, family and superfamily, describe near and distant evolutionary

W.S.: Mismatch string kernels for SVM protein classification

by Christina Leslie, Jason Weston, Eleazar Eskin, William Stafford Noble - In: Proc. of NIPS. (2003
"... We introduce a class of string kernels, called mismatch kernels, for use with support vector machines (SVMs) in a discriminative approach to the protein classification problem. These kernels measure sequence similarity based on shared occurrences of k-length subsequences, counted with up to m mismat ..."
Abstract - Cited by 155 (18 self) - Add to MetaCart
We introduce a class of string kernels, called mismatch kernels, for use with support vector machines (SVMs) in a discriminative approach to the protein classification problem. These kernels measure sequence similarity based on shared occurrences of k-length subsequences, counted with up to m
Results 1 - 10 of 1,317