NDLI: Towards the assessment of GRN algorithms based on (disease) ontology

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Towards the assessment of GRN algorithms based on (disease) ontology

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Towards the assessment of GRN algorithms based on (disease) ontology

Content Provider	ACM Digital Library
Author	Roy, Swarup Guzzi, Pietro H Milano, Marianna
Abstract	The inference of biological networks from experimental data is a growing area in bioinformatics and systems biology. Biological network inference aims to reconstruct network of interactions (or associations) among biological molecules (e.g. genes or proteins) starting from experimental observations. The growing of the number of these algorithms has been not accompanied from the appearance of fair assessments and comparisons. Current assessments are usually based on the comparison of the algorithms using reference networks or gold standard dataset. Low attention has been posed on the ability of algorithms to reconstruct network that are related to diseases or phenotypes. It has been observed that highly connected or central genes in a GRN or PPI networks are essential genes and possible key players for disease diagnosis. Consequently we designed a novel assessment methodology that relies on the ranking of inferred network on the basis of the similarity of their top genes (i.e. hubs) with respect to diseases. The assessment is based on a three step methodology. Initially, we infer regulatory networks using few popular inference methods. Next, we analyse the topology of the network to determine a list of top central genes and finally we match those genes to disease specific ontologies. We hypothesise that more number of top ranked genes from a GRN inferred by a method is matching with disease ontologies more it is influential. The results show a marked change in the ranking of participating methods while assessing network inferability with respect to disease specific ontologies.
Starting Page	561
Ending Page	567
Page Count	7
File Format	PDF
ISBN	9781450338530
DOI	10.1145/2808719.2812218
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-09-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Ontologies Graph algorithms Assessments
Content Type	Text
Resource Type	Article

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