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Reporter Gene Assays

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Attention for Chapter: Data Mining and Computational Modeling of High-Throughput Screening Datasets
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Chapter title
Data Mining and Computational Modeling of High-Throughput Screening Datasets
Book title
Reporter Gene Assays
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7724-6_14
Pubmed ID
Book ISBNs
978-1-4939-7722-2, 978-1-4939-7724-6
Authors

Sean Ekins, Alex M. Clark, Krishna Dole, Kellan Gregory, Andrew M. Mcnutt, Anna Coulon Spektor, Charlie Weatherall, Nadia K. Litterman, Barry A. Bunin

Abstract

We are now seeing the benefit of investments made over the last decade in high-throughput screening (HTS) that is resulting in large structure activity datasets entering public and open databases such as ChEMBL and PubChem. The growth of academic HTS screening centers and the increasing move to academia for early stage drug discovery suggests a great need for the informatics tools and methods to mine such data and learn from it. Collaborative Drug Discovery, Inc. (CDD) has developed a number of tools for storing, mining, securely and selectively sharing, as well as learning from such HTS data. We present a new web based data mining and visualization module directly within the CDD Vault platform for high-throughput drug discovery data that makes use of a novel technology stack following modern reactive design principles. We also describe CDD Models within the CDD Vault platform that enables researchers to share models, share predictions from models, and create models from distributed, heterogeneous data. Our system is built on top of the Collaborative Drug Discovery Vault Activity and Registration data repository ecosystem which allows users to manipulate and visualize thousands of molecules in real time. This can be performed in any browser on any platform. In this chapter we present examples of its use with public datasets in CDD Vault. Such approaches can complement other cheminformatics tools, whether open source or commercial, in providing approaches for data mining and modeling of HTS data.

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The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 21 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 24%
Researcher 3 14%
Student > Ph. D. Student 3 14%
Student > Bachelor 2 10%
Professor 1 5%
Other 2 10%
Unknown 5 24%
Readers by discipline Count As %
Chemistry 4 19%
Pharmacology, Toxicology and Pharmaceutical Science 4 19%
Biochemistry, Genetics and Molecular Biology 2 10%
Computer Science 2 10%
Nursing and Health Professions 1 5%
Other 4 19%
Unknown 4 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 May 2018.
All research outputs
#15,508,366
of 23,047,237 outputs
Outputs from Methods in molecular biology
#5,406
of 13,196 outputs
Outputs of similar age
#269,891
of 442,433 outputs
Outputs of similar age from Methods in molecular biology
#597
of 1,499 outputs
Altmetric has tracked 23,047,237 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,196 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 442,433 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,499 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.