Journal article
Practical Outcomes of Applying Ensemble Machine Learning Classifiers to High-Throughput Screening (HTS) Data Analysis and Screening
Journal of chemical information and modeling, v 48(11), pp 2196-2206
01 Nov 2008
PMID: 18983143
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Over the years numerous papers have presented the effectiveness of various machine learning methods in analyzing drug, discovery biological screening data. The predictive performance of models developed using these methods has traditionally been evaluated by assessing performance of the developed models against a portion of the data randomly selected for holdout. It has been our experience that Such assessments, while widely practiced, result in an optimistic assessment. This paper describes the development of a series of ensemble-based decision tree models, shares our experience at various stages in the model development process. and presents the impact of such models when they are applied to vendor offerings and the forecasted compounds are acquired and screened in the relevant assays. We have seen that well developed models can significantly increase the hit-rates observed in FITS campaigns.
Metrics
Details
- Title
- Practical Outcomes of Applying Ensemble Machine Learning Classifiers to High-Throughput Screening (HTS) Data Analysis and Screening
- Creators
- Kirk Simmons - DuPontJohn Kinney - Drexel UniversityAaron Owens - Drexel UniversityDaniel A. Kleier - Drexel UniversityKaren Bloch - Drexel UniversityDave Argentar - Drexel UniversityAlicia Walsh - Drexel UniversityGanesh Vaidyanathan - Drexel University
- Publication Details
- Journal of chemical information and modeling, v 48(11), pp 2196-2206
- Publisher
- American Chemical Society; Washington, DC
- Number of pages
- 11
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- [Retired Faculty]
- Web of Science ID
- WOS:000261103700009
- Scopus ID
- 2-s2.0-57549086634
- Other Identifier
- 991019168749004721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Industry collaboration
- Domestic collaboration
- Web of Science research areas
- Chemistry, Medicinal
- Chemistry, Multidisciplinary
- Computer Science, Information Systems
- Computer Science, Interdisciplinary Applications