Open AccessProceedings Article
The Conjunctive Disjunctive Node Kernel
Dinh Tran-Van,Alessandro Sperduti,Fabrizio Costa +2 more
- 01 Jan 2017
- pp 257-262
4
TL;DR: This work proposes a graph kernel method that explicitly models the configuration of each gene’s context and shows that it is competitive w.r.t. state-of-the-art kernel approaches.
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Abstract: Gene-disease associations are inferred on the basis of similarities between the proteins encoded by genes. Biological relationships used to define similarities range from interacting proteins, proteins that participate in pathways and protein expression profiles. Though graph kernel methods have become a prominent approach for association prediction, most solutions are based on a notion of information diffusion that does not capture the specificity of different network parts. Here we propose a graph kernel method that explicitly models the configuration of each gene’s context. An empirical evaluation on several biological databases shows that our proposal is competitive w.r.t. state-of-the-art kernel approaches.
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Citations
Heterogeneous networks integration for disease-gene prioritization with node kernels.
TL;DR: A novel node kernel suitable for graphs with typed edges is employed that can generate a large number of discriminative features that can be efficiently processed by linear regularized machine learning classifiers.
21
Link Enrichment for Diffusion-Based Graph Node Kernels
Dinh Tran-Van,Alessandro Sperduti,Fabrizio Costa +2 more
- 11 Sep 2017
TL;DR: Here, the notion of link enrichment is introduced, that is, performing link prediction in order to improve the performance of diffusion-based kernels to solve gene-disease association problems.
4
•Proceedings Article
DEEP: Decomposition Feature Enhancement Procedure for Graphs
Dinh Tran-Van,Nicolò Navarin,Alessandro Sperduti +2 more
- 01 Jan 2018
TL;DR: A procedure that allows to transform a local graph kernel in a kernel for nodes in a single, huge graph to apply a specific instantiation to the task of disease gene prioritization from the bioinformatics domain, improving the state of the art in many diseases.
1
A framework for the definition of complex structured feature spaces
TL;DR: Experimental results on eight real-world graph datasets from different domains show that the proposed framework instances are able to get a statistically significant performance improvement over both the considered base kernels and framework instances previously defined in literature.
References
The human disease network
Kwang-Il Goh,Michael E. Cusick,David Valle,Barton Childs,Marc Vidal,Albert-László Barabási,Albert-László Barabási +6 more
TL;DR: This paper found that essential human genes are likely to encode hub proteins and are expressed widely in most tissues, while the vast majority of disease genes are non-essential and show no tendency to encoding hub proteins, and their expression pattern indicates that they are localized in the functional periphery of the network.
•Journal Article
human disease network
TL;DR: It is found that essential human genes are likely to encode hub proteins and are expressed widely in most tissues, suggesting that disease genes also would play a central role in the human interactome, and that diseases caused by somatic mutations should not be peripheral.
2.9K
Gene Ontology Consortium. The Gene Ontology (GO) database and informatics resource
Harris Ma,Jennifer I. Clark,Amelia Ireland,Jane Lomax,Michael Ashburner,R. Foulger,Karen Eilbeck,Suzanna E. Lewis,B. Marshall,Christopher J. Mungall,J. Richter,Gerald M. Rubin,Blake Ja,Carol J. Bult,Mary E. Dolan,H. Drabkin,Janan T. Eppig,David P. Hill,L. Ni,Martin Ringwald,Rama Balakrishnan,J. M. Cherry,Karen R. Christie,Maria C. Costanzo,Selina S. Dwight,Stacia R. Engel,Dianna G. Fisk,Jodi E. Hirschman,Eurie L. Hong,Robert S. Nash,Anand Sethuraman,Chandra L. Theesfeld,David Botstein,Kara Dolinski,Becket Feierbach,Tanya Z. Berardini,S. Mundodi,Seung Y. Rhee,Rolf Apweiler,Daniel Barrell,Evelyn Camon,E. Dimmer,V. Lee,Rex L. Chisholm,Pascale Gaudet,Warren A. Kibbe,Ranjana Kishore,Erich M. Schwarz,Paul W. Sternberg,M. Gwinn,Linda Hannick,Jennifer R. Wortman,Matthew Berriman,Valerie Wood,N. de sur la Cruz,Peter J. Tonellato,Pankaj Jaiswal,Trent E. Seigfried,Ra White +58 more
- 01 Jan 2004
TL;DR: The Gene Ontology (GO) project provides structured, controlled vocabularies and classifications that cover several domains of molecular and cellular biology and are freely available for community use in the annotation of genes, gene products and sequences.
2.7K
Convolution kernels on discrete structures
David Haussler
- 01 Jan 1999
TL;DR: A new method of constructing kernels on sets whose elements are discrete structures like strings, trees and graphs is introduced, which can be applied iteratively to build a kernel on a innnite set from kernels involving generators of the set.
1.4K
•Proceedings Article
Diffusion Kernels on Graphs and Other Discrete Input Spaces
Risi Kondor,John Lafferty +1 more
- 08 Jul 2002
TL;DR: This paper proposes a general method of constructing natural families of kernels over discrete structures, based on the matrix exponentiation idea, and focuses on generating kernels on graphs, for which a special class of exponential kernels called diffusion kernels are proposed.