A shortest-path graph kernel for estimating gene product semantic similarity

Álvarez, Marco; Qi, Xiaojun; Yan, Cong

doi:10.1186/2041-1480-2-3

Cited by 34 publications

(21 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Graph kernel can be seen as a function that measures the topological similarity of pairs of graphs. In recent years, lots of methods have been proposed to construct graph kernel, which include walk-based (Gartner et al, 2003), path-based (Alvarez et al, 2011), subtree-based kernels (Shervashidze et al, 2011), and so on. Graph kernel has been widely used for image classification (Harchaoui and Bach, 2007) and protein function prediction (Borgwardt et al, 2005).…”

Section: Methodsmentioning

confidence: 99%

Frequent and Discriminative Subnetwork Mining for Mild Cognitive Impairment Classification

Fei

Jie²,

Zhang³

2014

Brain Connectivity

View full text Add to dashboard Cite

Recent studies on brain networks have suggested that many brain diseases, such as Alzheimer's disease and mild cognitive impairment (MCI), are related to a large-scale brain network, rather than individual brain regions. However, it is challenging to find such a network from the whole brain network due to the complexity of brain networks. In this article, the authors propose a novel method to mine the discriminative subnetworks for classifying MCI patients from healthy controls (HC). Specifically, the authors first extract a set of frequent subnetworks from each of the two groups (i.e., MCI and HC), respectively. Then, measure the discriminative ability of those frequent subnetworks using the graph kernel-based classification method and select the most discriminative subnetworks for subsequent classification. The results on the functional connectivity networks of 12 MCI and 25 HC show that this method can obtain competitive results compared with state-of-the-art methods on MCI classification.

show abstract

Section: Methodsmentioning

confidence: 99%

Frequent and Discriminative Subnetwork Mining for Mild Cognitive Impairment Classification

Fei

Jie²,

Zhang³

2014

Brain Connectivity

View full text Add to dashboard Cite

show abstract

“…We can normalize this method by the union of two GO term sets annotating the two proteins. Third, a graph-based method [34] is a combination of the edge-based and nodebased methods. This method captures a subgraph of the ontology which includes all the GO terms annotating a protein, and measures similarity between the two subgraphs formed by the two proteins.…”

Section: Ppi Weightingmentioning

confidence: 99%

P-Finder: Reconstruction of Signaling Networks from Protein-Protein Interactions and GO Annotations

Cho

Xin

Speegle

2015

IEEE/ACM Trans. Comput. Biol. and Bioinf.

View full text Add to dashboard Cite

Because most complex genetic diseases are caused by defects of cell signaling, illuminating a signaling cascade is essential for understanding their mechanisms. We present three novel computational algorithms to reconstruct signaling networks between a starting protein and an ending protein using genome-wide protein-protein interaction (PPI) networks and gene ontology (GO) annotation data. A signaling network is represented as a directed acyclic graph in a merged form of multiple linear pathways. An advanced semantic similarity metric is applied for weighting PPIs as the preprocessing of all three methods. The first algorithm repeatedly extends the list of nodes based on path frequency towards an ending protein. The second algorithm repeatedly appends edges based on the occurrence of network motifs which indicate the link patterns more frequently appearing in a PPI network than in a random graph. The last algorithm uses the information propagation technique which iteratively updates edge orientations based on the path strength and merges the selected directed edges. Our experimental results demonstrate that the proposed algorithms achieve higher accuracy than previous methods when they are tested on well-studied pathways of S. cerevisiae. Furthermore, we introduce an interactive web application tool, called P-Finder, to visualize reconstructed signaling networks.

show abstract

“…One of the most successful and widely used approach to defining kernels between a pair of graphs is to decompose the graphs into substructures and to compare/count pairs of specific isomorphic substructures. Specifically, all available graph decomposition methods can be used to define a graph kernel, e.g., graph kernels based on counting pairs of isomorphic a) walks [7], b) paths [8], and c) restricted subgraph or subtree substructures [9]. With this scenario, Gaidon et al [10] have developed a subtree kernel for comparing videos.…”

Section: A Graph Kernels On Computer Visionmentioning

confidence: 99%

Shape classification with a vertex clustering graph kernel

Bai

Cui

Wang

et al. 2016

2016 23rd International Conference on Pattern Recognition (ICPR)

View full text Add to dashboard Cite

Abstract-Graph kernels are powerful tools for structural analysis in computer vision. Unfortunately, most existing stateof-the-art graph kernels ignore the locational or structural correspondence information between graphs, based on the visual background. This drawback influences the performance of existing kernels for computer vision based classification problems, e.g., classification of shapes, point clouds and digital images. The aim of this paper is to address the problem with existing kernels, by developing a novel vertex clustering graph kernel. We show that this kernel not only overcomes the shortcoming of ignoring correspondence information between isomorphic substructures that arises in most existing graph kernels, but also guarantees the transitivity between the correspondence information. Our kernel can easily outperform state-of-the-art graph kernels in terms of classification accuracy on standard shape based graph datasets.

show abstract

A shortest-path graph kernel for estimating gene product semantic similarity

Cited by 34 publications

References 22 publications

Frequent and Discriminative Subnetwork Mining for Mild Cognitive Impairment Classification

Frequent and Discriminative Subnetwork Mining for Mild Cognitive Impairment Classification

P-Finder: Reconstruction of Signaling Networks from Protein-Protein Interactions and GO Annotations

Shape classification with a vertex clustering graph kernel

Contact Info

Product

Resources

About