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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.

Twitter Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 38%
Researcher 3 38%
Other 1 13%
Student > Ph. D. Student 1 13%
Readers by discipline Count As %
Chemistry 3 38%
Computer Science 2 25%
Pharmacology, Toxicology and Pharmaceutical Science 2 25%
Agricultural and Biological Sciences 1 13%

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
#8,105,727
of 12,923,846 outputs
Outputs from Methods in molecular biology
#2,904
of 8,419 outputs
Outputs of similar age
#160,585
of 269,735 outputs
Outputs of similar age from Methods in molecular biology
#4
of 5 outputs
Altmetric has tracked 12,923,846 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,419 research outputs from this source. They receive a mean Attention Score of 2.1. This one has gotten more attention than average, scoring higher than 55% of its peers.
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 269,735 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one.