University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Analysis of mass spectrometry data from the secretome of an explant model of articular cartilage exposed to pro-inflammatory and anti-inflammatory stimuli using machine learning.

Swan, AL, Hillier, KL, Smith, JR, Allaway, D, Liddell, S, Bacardit, J and Mobasheri, A (2013) Analysis of mass spectrometry data from the secretome of an explant model of articular cartilage exposed to pro-inflammatory and anti-inflammatory stimuli using machine learning. BMC MUSCULOSKELETAL DISORDERS, 14.

[img]
Preview
Text
Analysis of mass spectrometry data from the secretome of an explant model of articular cartilage exposed to pro-inflammatory and anti-inflammatory stimuli using machine learning..pdf - ["content_typename_Published version (Publisher's proof or final PDF)" not defined]
Available under License : See the attached licence file.

Download (1MB) | Preview
[img]
Preview
PDF (licence)
SRI_deposit_agreement.pdf
Available under License : See the attached licence file.

Download (33kB) | Preview

Abstract

BACKGROUND: Osteoarthritis (OA) is an inflammatory disease of synovial joints involving the loss and degeneration of articular cartilage. The gold standard for evaluating cartilage loss in OA is the measurement of joint space width on standard radiographs. However, in most cases the diagnosis is made well after the onset of the disease, when the symptoms are well established. Identification of early biomarkers of OA can facilitate earlier diagnosis, improve disease monitoring and predict responses to therapeutic interventions. METHODS: This study describes the bioinformatic analysis of data generated from high throughput proteomics for identification of potential biomarkers of OA. The mass spectrometry data was generated using a canine explant model of articular cartilage treated with the pro-inflammatory cytokine interleukin 1 β (IL-1β). The bioinformatics analysis involved the application of machine learning and network analysis to the proteomic mass spectrometry data. A rule based machine learning technique, BioHEL, was used to create a model that classified the samples into their relevant treatment groups by identifying those proteins that separated samples into their respective groups. The proteins identified were considered to be potential biomarkers. Protein networks were also generated; from these networks, proteins pivotal to the classification were identified. RESULTS: BioHEL correctly classified eighteen out of twenty-three samples, giving a classification accuracy of 78.3% for the dataset. The dataset included the four classes of control, IL-1β, carprofen, and IL-1β and carprofen together. This exceeded the other machine learners that were used for a comparison, on the same dataset, with the exception of another rule-based method, JRip, which performed equally well. The proteins that were most frequently used in rules generated by BioHEL were found to include a number of relevant proteins including matrix metalloproteinase 3, interleukin 8 and matrix gla protein. CONCLUSIONS: Using this protocol, combining an in vitro model of OA with bioinformatics analysis, a number of relevant extracellular matrix proteins were identified, thereby supporting the application of these bioinformatics tools for analysis of proteomic data from in vitro models of cartilage degradation.

Item Type: Article
Divisions : Faculty of Health and Medical Sciences > School of Veterinary Medicine
Authors :
AuthorsEmailORCID
Swan, ALUNSPECIFIEDUNSPECIFIED
Hillier, KLUNSPECIFIEDUNSPECIFIED
Smith, JRUNSPECIFIEDUNSPECIFIED
Allaway, DUNSPECIFIEDUNSPECIFIED
Liddell, SUNSPECIFIEDUNSPECIFIED
Bacardit, JUNSPECIFIEDUNSPECIFIED
Mobasheri, AUNSPECIFIEDUNSPECIFIED
Date : 13 December 2013
Identification Number : 10.1186/1471-2474-14-349
Uncontrolled Keywords : Animals, Artificial Intelligence, Cartilage, Articular, Dogs, Interleukin-1beta, Male, Mass Spectrometry, Osteoarthritis, Proteins, Proteome
Related URLs :
Additional Information : © 2013 Swan et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Depositing User : Symplectic Elements
Date Deposited : 23 Dec 2015 11:48
Last Modified : 23 Dec 2015 11:48
URI: http://epubs.surrey.ac.uk/id/eprint/809563

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800