Literature DB >> 18217738

Flexible Web service infrastructure for the development and deployment of predictive models.

Rajarshi Guha1.   

Abstract

The development of predictive statistical models is a common task in the field of drug design. The process of developing such models involves two main steps: building the model and then deploying the model. Traditionally such models have been deployed using Web page interfaces. This approach restricts the user to using the specified Web page, and using the model in other ways can be cumbersome. In this paper we present a flexible and generalizable approach to the deployment of predictive models, based on a Web service infrastructure using R. The infrastructure described allows one to access the functionality of these models using a variety of approaches ranging from Web pages to workflow tools. We highlight the advantages of this infrastructure by developing and subsequently deploying random forest models for two data sets.

Mesh:

Year:  2008        PMID: 18217738     DOI: 10.1021/ci700188u

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  3 in total

1.  Investigating the correlations among the chemical structures, bioactivity profiles and molecular targets of small molecules.

Authors:  Tiejun Cheng; Yanli Wang; Stephen H Bryant
Journal:  Bioinformatics       Date:  2010-10-13       Impact factor: 6.937

2.  Data mining the NCI60 to predict generalized cytotoxicity.

Authors:  Adam C Lee; Kerby Shedden; Gustavo R Rosania; Gordon M Crippen
Journal:  J Chem Inf Model       Date:  2008-06-28       Impact factor: 4.956

Review 3.  Fragment-based screening with natural products for novel anti-parasitic disease drug discovery.

Authors:  Miaomiao Liu; Ronald J Quinn
Journal:  Expert Opin Drug Discov       Date:  2019-09-12       Impact factor: 6.098

  3 in total

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