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AP Dr Ts Izzatdin Abdul Aziz
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B.Sc. (Computer Science)
Master of Computer Applications
PhD
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Joined UTP
November, 2014
2021
Kumar, Rajnish; Khan, Farhat Ullah; Sharma, Anju; Siddiqui, Mohammed Haris; Aziz, Izzatdin Abdul; Kamal, Mohammad Amjad; Ashraf, Ghulam Md; Alghamdi, Badrah S; Uddin, Md Sahab
Ä deep neural network-based approach for prediction of mutagenicity of compounds Journal Article
In: Environ. Sci. Pollut. Res. Int., vol. 28, no. 34, pp. 47641–47650, 2021.
Abstract | BibTeX | Tags: Deep learning; Deep neural network; Environmental exposure; Machine learning; Mutagen; Prediction
@article{Kumar2021-ky,
title = {Ä deep neural network-based approach for prediction of mutagenicity of compounds},
author = {Rajnish Kumar and Farhat Ullah Khan and Anju Sharma and Mohammed Haris Siddiqui and Izzatdin Abdul Aziz and Mohammad Amjad Kamal and Ghulam Md Ashraf and Badrah S Alghamdi and Md Sahab Uddin},
year = {2021},
date = {2021-09-01},
urldate = {2021-09-01},
journal = {Environ. Sci. Pollut. Res. Int.},
volume = {28},
number = {34},
pages = {47641--47650},
publisher = {Springer Science and Business Media LLC},
abstract = {We are exposed to various chemical compounds present in the
environment, cosmetics, and drugs almost every day. Mutagenicity
is a valuable property that plays a significant role in
establishing a chemical compound's safety. Exposure and handling
of mutagenic chemicals in the environment pose a high health
risk; therefore, identification and screening of these chemicals
are essential. Considering the time constraints and the pressure
to avoid laboratory animals' use, the shift to alternative
methodologies that can establish a rapid and cost-effective
detection without undue over-conservation seems critical. In
this regard, computational detection and identification of the
mutagens in environmental samples like drugs, pesticides, dyes,
reagents, wastewater, cosmetics, and other substances is vital.
From the last two decades, there have been numerous efforts to
develop the prediction models for mutagenicity, and by far,
machine learning methods have demonstrated some noteworthy
performance and reliability. However, the accuracy of such
prediction models has always been one of the major concerns for
the researchers working in this area. The mutagenicity
prediction models were developed using deep neural network
(DNN), support vector machine, k-nearest neighbor, and random
forest. The developed classifiers were based on 3039 compounds
and validated on 1014 compounds; each of them encoded with 1597
molecular feature vectors. DNN-based prediction model yielded
highest prediction accuracy of 92.95% and 83.81% with the
training and test data, respectively. The area under the
receiver's operating curve and precision-recall curve values
were found to be 0.894 and 0.838, respectively. The DNN-based
classifier not only fits the data with better performance as
compared to traditional machine learning algorithms, viz.,
support vector machine, k-nearest neighbor, and random forest
(with and without feature reduction) but also yields better
performance metrics. In current work, we propose a DNN-based
model to predict mutagenicity of compounds.},
keywords = {Deep learning; Deep neural network; Environmental exposure; Machine learning; Mutagen; Prediction},
pubstate = {published},
tppubtype = {article}
}
environment, cosmetics, and drugs almost every day. Mutagenicity
is a valuable property that plays a significant role in
establishing a chemical compound's safety. Exposure and handling
of mutagenic chemicals in the environment pose a high health
risk; therefore, identification and screening of these chemicals
are essential. Considering the time constraints and the pressure
to avoid laboratory animals' use, the shift to alternative
methodologies that can establish a rapid and cost-effective
detection without undue over-conservation seems critical. In
this regard, computational detection and identification of the
mutagens in environmental samples like drugs, pesticides, dyes,
reagents, wastewater, cosmetics, and other substances is vital.
From the last two decades, there have been numerous efforts to
develop the prediction models for mutagenicity, and by far,
machine learning methods have demonstrated some noteworthy
performance and reliability. However, the accuracy of such
prediction models has always been one of the major concerns for
the researchers working in this area. The mutagenicity
prediction models were developed using deep neural network
(DNN), support vector machine, k-nearest neighbor, and random
forest. The developed classifiers were based on 3039 compounds
and validated on 1014 compounds; each of them encoded with 1597
molecular feature vectors. DNN-based prediction model yielded
highest prediction accuracy of 92.95% and 83.81% with the
training and test data, respectively. The area under the
receiver's operating curve and precision-recall curve values
were found to be 0.894 and 0.838, respectively. The DNN-based
classifier not only fits the data with better performance as
compared to traditional machine learning algorithms, viz.,
support vector machine, k-nearest neighbor, and random forest
(with and without feature reduction) but also yields better
performance metrics. In current work, we propose a DNN-based
model to predict mutagenicity of compounds.