Multiple graph regularized protein domain ranking

@article{Wang2012MultipleGR,
  title={Multiple graph regularized protein domain ranking},
  author={Jingyan Wang and Halima Bensmail and Xin Gao},
  journal={BMC Bioinformatics},
  year={2012},
  volume={13},
  pages={307 - 307}
}
BackgroundProtein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and… 
View on Springer
arxiv.org
53 Citations
Highly Influential Citations
2
Background Citations
12
Methods Citations
21

53 Citations

CMsearch: simultaneous exploration of protein sequence space and structure space improves not only protein homology detection but also protein structure prediction

This work presents a novel network-based protein homology detection method, CMsearch, based on cross-modal learning, which clearly outperforms state-of-the-art homology Detection methods and the CASP-winning template-basedprotein structure prediction methods.

K-nearest uphill clustering in the protein structure space

Deep Multi-Graph Clustering via Attentive Cross-Graph Association

An end-to-end deep learning model is proposed to simultaneously infer cluster assignments and cluster associations in multi-graph using autoencoding networks to learn node embeddings and a minimum-entropy based clustering strategy to cluster nodes in the embedding space for each graph.

Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization

Low rank approximation with sparse integration of multiple manifolds for data representation

This paper deals with the issue of searching for the optimal linear combination space of multiple graphs under the low rank matrix approximation model and efficient projection onto the probabilistic simplex is utilized to optimize the coefficient vector of graphs, resulting in the sparse pattern of coefficients.

When sparse coding meets ranking: a joint framework for learning sparse codes and ranking scores

This paper develops the first joint sparse coding and ranking score learning algorithm, which assumes that in the neighborhood of each data point, the ranking scores can be approximated from the corresponding sparse codes by a local linear function.

When coding meets ranking: A joint framework based on local learning

This paper develops the first joint sparse coding and ranking score learning algorithm, which assumes that in the neighborhood of each data point, the ranking scores can be approximated from the corresponding sparse codes by a local linear function.

Bipartite graphs in systems biology and medicine: a survey of methods and applications

In this review, special focus is given to the usability of bipartite graphs and their impact on the field of network biology and medicine and how their topological properties can be applied to certain biological case studies.

Feature Dimension Reduction and Graph Based Ranking Based Image Classification

The graph based similarity learning algorithm which tries to exploit contextual information for improving the effectiveness of image rankings is proposed for image class and retrieval problem and is compared with other approaches on an image database.

Image Classification by Feature Dimension Reduction and Graph based Ranking

The graph based similarity learning algorithm which tries to exploit contextual information for improving the effectiveness of image rankings is proposed for image class and retrieval problem and is compared with other approaches on an image database.
...

References

SHOWING 1-10 OF 49 REFERENCES

A pr 2 01 3 Multiple graph regularized protein domain ranking

The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs by developing the Multiple Graph regularized Ranking algorithm, MultiGRank.

Protein Ranking by Semi-Supervised Network Propagation

It is shown here that labeled data can be used to learn a network without any need for estimating parameters of the transfer function, and that diffusion on this learned network produces better results than the original RankProp algorithm with a fixed network.

Using context to improve protein domain identification

The framework, Domain Prediction Using Context (dPUC), incorporates pairwise "context" scores between domains, along with traditional domain scores and thresholds, and improves domain prediction across a variety of organisms from bacteria to protozoa and metazoa.

Sequential Linear Neighborhood Propagation for Semi-Supervised protein Function Prediction

A new graph-based semi-supervised classification algorithm Sequential Linear Neighborhood Propagation (SLNP) is introduced, which addresses the problem of the classification of partially labeled protein interaction networks.

Semi-supervised Protein Function Prediction via Sequential Linear Neighborhood Propagation

A new graph-based semi-supervised classification algorithm Sequential Linear Neighborhood Propagation (SLNP) is introduced, which addresses the problem of the classification of partially labeled protein interaction networks.

ProDis-ContSHC: learning protein dissimilarity measures and hierarchical context coherently for protein-protein comparison in protein database retrieval

Experimental results demonstrate that plugging the supervised contextual Dissimilarity measures into the retrieval systems significantly outperforms the context-free dissimilarity/similarity measures and other unsupervised contextual dissimilarities measures that do not use the class label information.

Tableau-based protein substructure search using quadratic programming

An improved method for detecting substructural similarities in proteins using tableaux is developed, including constraints based on the separation of secondary structure elements that increases the accuracy of protein structure search using maximally-similar subtableau extraction to a level where it has comparable or superior accuracy to existing techniques.

A fast indexing approach for protein structure comparison

IR Tableau, a fast protein comparison algorithm, which leverages the tableau representation to compare protein tertiary structures, shows that it is possible to obtain very significant speedups for the protein structure comparison problem, by employing an information retrieval style approach for indexing proteins.

Protein structure alignment using environmental profiles.

A new protein structure alignment procedure is described, which is fast enough to do all-against-all structural comparisons routinely and sometimes finds an alignment that suggests an evolutionary relationship and which is not normally obtained if only geometry is considered.

Fast SCOP Classification of Structural Class and Fold Using Secondary Structure Mining in Distance Matrix

A novel method of rapid domain classification that can perform effectively and efficiently automatic classification of domain with the benefit of low dimension and meaningful features, but also no need of complicated classifier system is introduced.