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A Predictive Model for Toxicity Effects Assessment of Biotransformed Hepatic Drugs Using Iterative Sampling Method

Overview of attention for article published in Scientific Reports, December 2016
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Title
A Predictive Model for Toxicity Effects Assessment of Biotransformed Hepatic Drugs Using Iterative Sampling Method
Published in
Scientific Reports, December 2016
DOI 10.1038/srep38660
Pubmed ID
Authors

Alaa Tharwat, Yasmine S. Moemen, Aboul Ella Hassanien

Abstract

Measuring toxicity is one of the main steps in drug development. Hence, there is a high demand for computational models to predict the toxicity effects of the potential drugs. In this study, we used a dataset, which consists of four toxicity effects:mutagenic, tumorigenic, irritant and reproductive effects. The proposed model consists of three phases. In the first phase, rough set-based methods are used to select the most discriminative features for reducing the classification time and improving the classification performance. Due to the imbalanced class distribution, in the second phase, different sampling methods such as Random Under-Sampling, Random Over-Sampling and Synthetic Minority Oversampling Technique are used to solve the problem of imbalanced datasets. ITerative Sampling (ITS) method is proposed to avoid the limitations of those methods. ITS method has two steps. The first step (sampling step) iteratively modifies the prior distribution of the minority and majority classes. In the second step, a data cleaning method is used to remove the overlapping that is produced from the first step. In the third phase, Bagging classifier is used to classify an unknown drug into toxic or non-toxic. The experimental results proved that the proposed model performed well in classifying the unknown samples according to all toxic effects in the imbalanced datasets.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 16%
Student > Bachelor 6 16%
Student > Master 5 14%
Researcher 5 14%
Student > Doctoral Student 1 3%
Other 4 11%
Unknown 10 27%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 14%
Computer Science 5 14%
Pharmacology, Toxicology and Pharmaceutical Science 4 11%
Engineering 3 8%
Agricultural and Biological Sciences 2 5%
Other 4 11%
Unknown 14 38%
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 09 December 2016.
All research outputs
#20,363,191
of 22,912,409 outputs
Outputs from Scientific Reports
#105,804
of 123,793 outputs
Outputs of similar age
#353,521
of 419,352 outputs
Outputs of similar age from Scientific Reports
#2,964
of 3,432 outputs
Altmetric has tracked 22,912,409 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 123,793 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.2. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 3,432 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.