English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
 Local TagsRelease HistoryDetailsSummary
  Machine Learning approaches to protein ranking: discriminative, semi-supervised, scalable algorithms

Weston, J., Leslie, C., Elisseeff, A., & Noble, W.(2003). Machine Learning approaches to protein ranking: discriminative, semi-supervised, scalable algorithms (111). Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

Item is

 

show all hide all

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-DC53-8 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-8C9A-3
Genre: Report

Files

show Files
hide Files
:
MPIK-TR-111.pdf (Publisher version), 412KB
View   Save
File Permalink:
http://hdl.handle.net/21.11116/0000-0002-8C9B-2
Name:
MPIK-TR-111.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
View
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Weston, J1, 2, Author              
Leslie, C, Author
Elisseeff, A1, 2, Author              
Noble, WS, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

Content

show
hide
Free keywords: -
 Abstract: A key tool in protein function discovery is the ability to rank databases of proteins given a query amino acid sequence. The most successful method so far is a web-based tool called PSI-BLAST which uses heuristic alignment of a profile built using the large unlabeled database. It has been shown that such use of global information via an unlabeled data improves over a local measure derived from a basic pairwise alignment such as performed by PSI-BLAST's predecessor, BLAST. In this article we look at ways of leveraging techniques from the field of machine learning for the problem of ranking. We show how clustering and semi-supervised learning techniques, which aim to capture global structure in data, can significantly improve over PSI-BLAST.

Details

show
hide
Language(s):
 Dates: 2003-06
 Publication Status: Published in print
 Pages: 10
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
 Table of Contents: -
 Rev. Type: -
 Identifiers: Report Nr.: 111
BibTex Citekey: 2300
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Technical Report of the Max Planck Institute for Biological Cybernetics
Source Genre: Series
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 111 Sequence Number: - Start / End Page: - Identifier: -