English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
 Local TagsRelease HistoryDetailsSummary
  A Linear Programming Approach for Molecular QSAR analysis

Saigo, H., Kadowaki, T., & Tsuda, K. (2009). A Linear Programming Approach for Molecular QSAR analysis. In T. Gärtner, G. Garriga, & T. Meinl (Eds.), MLG 2006: Proceedings of the International Workshop on Mining and Learning with Graphs in conjunction with ECML/PKDD 2006 (pp. 85-96). Konstanz, Germany: Bibliothek der Universität Konstanz.

Item is

 

show all hide all

Basic

show hide
Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D03B-6 Version Permalink: https://hdl.handle.net/21.11116/0000-0004-96DD-A
Genre: Conference Paper

Files

show Files
hide Files
:
MLG-2006-Saigo.pdf (Any fulltext), 207KB
View   Save
File Permalink:
https://hdl.handle.net/21.11116/0000-0004-96DE-9
Name:
MLG-2006-Saigo.pdf
Description:
-
OA-Status:
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
View
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show
hide
Locator:
http://kops.uni-konstanz.de/bitstream/handle/123456789/5412/MLG2006_Proceedings.pdf (Publisher version)
Description:
-
OA-Status:

Creators

show
hide
 Creators:
Saigo, H1, 2, Author           
Kadowaki, T, Author
Tsuda, K1, 2, 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: Small molecules in chemistry can be represented as graphs.
In a quantitative structure-activity relationship (QSAR) analysis, the
central task is to find a regression function that predicts
the activity of the molecule in high accuracy.
Setting a QSAR as a primal target, we propose a new linear
programming approach to the graph-based regression problem.
Our method extends the graph classification algorithm by Kudo et al.
(NIPS 2004), which is a combination of boosting and graph mining.
Instead of sequential multiplicative updates, we employ the linear
programming boosting (LP) for regression. The LP approach allows to
include inequality constraints for the parameter vector, which turns out to
be particularly useful in QSAR tasks where activity values are
sometimes unavailable.
Furthermore, the efficiency is improved significantly by employing
multiple pricing.

Details

show
hide
Language(s):
 Dates: 2009
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 4160
 Degree: -

Event

show
hide
Title: International Workshop on Mining and Learning with Graphs 2006 (MLG 2006)
Place of Event: Berlin, Germany
Start-/End Date: 2006-09-18

Legal Case

show

Project information

show

Source 1

show
hide
Title: MLG 2006: Proceedings of the International Workshop on Mining and Learning with Graphs in conjunction with ECML/PKDD 2006
Source Genre: Proceedings
 Creator(s):
Gärtner, T, Editor
Garriga, GC, Editor
Meinl, T, Editor
Affiliations:
-
Publ. Info: Konstanz, Germany : Bibliothek der Universität Konstanz
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 85 - 96 Identifier: -