Skip to main content

Predicting Post-Synaptic Activity in Proteins with Data Mining

Pappa, Gisele L., Baines, Anthony J., Freitas, Alex A. (2005) Predicting Post-Synaptic Activity in Proteins with Data Mining. Bioinformatics, 21 (Suppl.). ii19-ii25. ISSN 1367-4803. (doi:10.1093/bioinformatics/bti1102) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:14254)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://dx.doi.org/10.1093/bioinformatics/bti1102

Abstract

The bioinformatics problem being addressed in this paper is to predict whether or not a protein has post-synaptic activity. This problem is of great intrinsic interest because proteins with post-synaptic activities are connected with functioning of the nervous system. Indeed, many proteins having post-synaptic activity have been functionally characterized by biochemical, immunological and proteomic exercises. They represent a wide variety of proteins with functions in extracellular signal reception and propagation through intracellular apparatuses, cell adhesion molecules and scaffolding proteins that link them in a web. The challenge is to automatically discover features of the primary sequences of proteins that typically occur in proteins with post-synaptic activity but rarely (or never) occur in proteins without post-synaptic activity, and vice-versa. In this context, we used data mining to automatically discover classification rules that predict whether or not a protein has post-synaptic activity. The discovered rules were analysed with respect to their predictive accuracy (generalization ability) and with respect to their interestingness to biologists (in the sense of representing novel, unexpected knowledge).

Item Type: Article
DOI/Identification number: 10.1093/bioinformatics/bti1102
Uncontrolled keywords: data mining, bioinformatics, classification rules
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:02 UTC
Last Modified: 16 Nov 2021 09:52 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/14254 (The current URI for this page, for reference purposes)

University of Kent Author Information

Baines, Anthony J..

Creator's ORCID:
CReDIT Contributor Roles:

Freitas, Alex A..

Creator's ORCID: https://orcid.org/0000-0001-9825-4700
CReDIT Contributor Roles:
  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.