The ASTRAL compendium for protein structure and sequence analysis

Brenner, Steven E.; Koehl, Patrice; Levitt, Michael

doi:10.1093/nar/28.1.254

Cited by 454 publications

(370 citation statements)

References 32 publications

Supporting

Mentioning

366

Contrasting

Order By: Relevance

“…This is an unsupervised learning method that computes low dimensional, neighbourhood preserving embeddings of high dimensional data. We used this on high dimensional datasets like the leukaemia dataset and SCOP [14]. Details of this method and the Matlab code of the LLE are available at http://www.cs.toronto.edu/~roweis/lle/.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Kernel-Based Classification of Tissues Using Feature Weightings

Kertész‐Farkas

Kocsor²

2006

Appl Ecol Env Res

View full text Add to dashboard Cite

Kertész-Farkas -Kocsor: Kernel-based classification of tissues using feature weightings - Abstract. In high-dimensional spaces classification methods could be more effective using various feature selection methods. The training procedure could be speeded up by decreasing the dimension of the feature space, and the classification method could be improved by removing noisy or irrelevant features. In this paper we present a new method which weights the features according to their importance instead of removing the negligible ones via kernel functions. It could be applied to a range of real-world problems. We tested it on several biological datasets like a small part of the UCI Learning Repository and SCOP and the Leukaemia AML-ALL databases, and obtained a significantly better classification performance than that using the usual unweighted method.

show abstract

Section: Resultsmentioning

confidence: 99%

“…The sequences were selected using the Astral databases (http://astral.stanford.edu) [14]. Here, positive training examples are chosen from the remaining families in the same superfamily, and negative test and training examples are chosen from disjoint sets of folds outside the target family's fold [12].…”

Section: Tests On the Scop Databasesmentioning

confidence: 99%

Kernel-Based Classification of Tissues Using Feature Weightings

Kertész‐Farkas

Kocsor²

2006

Appl Ecol Env Res

View full text Add to dashboard Cite

show abstract

“…We first extract octapeptide helical local conformations from the proteins in ASTRAL 95 dataset version(1.67) [11] as described in [12]. The helices are described using geometric invariant structure properties such as edge, perimeter, volume, area of triangle etc.…”

Section: Methodsmentioning

confidence: 99%

Characterization and sequence prediction of structural variations in α-helix

Tendulkar

Wangikar

2011

BMC Bioinformatics

View full text Add to dashboard Cite

BackgroundThe structure conservation in various α-helix subclasses reveals the sequence and context dependent factors causing distortions in the α-helix. The sequence-structure relationship in these subclasses can be used to predict structural variations in α-helix purely based on its sequence. We train support vector machine(SVM) with dot product kernel function to discriminate between regular α-helix and non-regular α-helices purely based on the sequences, which are represented with various overall and position specific propensities of amino acids.ResultsWe characterize the structural distortions in five α-helix subclasses. The sequence structure correlation in the subclasses reveals that the increased propensity of proline, histidine, serine, aspartic acid and aromatic amino acids are responsible for the distortions in regular α-helix. The N-terminus of regular α-helix prefers neutral and acidic polar amino acids, while the C-terminus prefers basic polar amino acid. Proline is preferred in the first turn of regular α-helix , while it is preferred to produce kinked and curved subclasses. The SVM discriminates between regular α-helix and the rest with precision of 80.97% and recall of 88.05%.ConclusionsThe correlation between structural variation in helices and their sequences is manifested by the performance of SVM based on sequence features. The results presented here are useful for computational design of helices. The results are also useful for prediction of structural perturbations in helix sequence purely based on its sequence.

show abstract

“…We used ASTRAL (Brenner et al 2000) to filter these sequences so that no two sequences share greater than 95% identity. The class labels are organized in a 3-level hierarchy, consisting of protein folds, superfamilies and families (in descending order).…”

Section: Astral 2 Andmentioning

confidence: 99%

Efficient prediction algorithms for binary decomposition techniques

Park

Fürnkranz

2011

Data Min Knowl Disc

View full text Add to dashboard Cite

Binary decomposition methods transform multiclass learning problems into a series of two-class learning problems that can be solved with simpler learning algorithms. As the number of such binary learning problems often grows super-linearly with the number of classes, we need efficient methods for computing the predictions. In this paper, we discuss an efficient algorithm that queries only a dynamically determined subset of the trained classifiers, but still predicts the same classes that would have been predicted if all classifiers had been queried. The algorithm is first derived for the simple case of pairwise classification, and then generalized to arbitrary pairwise decompositions of the learning problem in the form of ternary error-correcting output codes under a variety of different code designs and decoding strategies.

show abstract

The ASTRAL compendium for protein structure and sequence analysis

Cited by 454 publications

References 32 publications

Kernel-Based Classification of Tissues Using Feature Weightings

Kernel-Based Classification of Tissues Using Feature Weightings

Characterization and sequence prediction of structural variations in α-helix

Efficient prediction algorithms for binary decomposition techniques

Contact Info

Product

Resources

About