ORCID Profile
0000-0002-8566-0870
Current Organisation
University of Newcastle Australia
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Publisher: Inderscience Publishers
Date: 2011
Publisher: IEEE
Date: 07-2016
Publisher: Public Library of Science (PLoS)
Date: 23-01-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 06-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2008
Publisher: Springer Japan
Date: 2013
Publisher: Springer Japan
Date: 2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2013
Publisher: Public Library of Science (PLoS)
Date: 14-01-2016
Publisher: Springer Science and Business Media LLC
Date: 30-10-2020
Publisher: Springer Science and Business Media LLC
Date: 28-06-2011
Abstract: With an increasing number of plant genome sequences, it has become important to develop a robust computational method for detecting plant promoters. Although a wide variety of programs are currently available, prediction accuracy of these still requires further improvement. The limitations of these methods can be addressed by selecting appropriate features for distinguishing promoters and non-promoters. In this study, we proposed two feature selection approaches based on hexamer sequences: the Frequency Distribution Analyzed Feature Selection Algorithm (FDAFSA) and the Random Triplet Pair Feature Selecting Genetic Algorithm (RTPFSGA). In FDAFSA, adjacent triplet-pairs (hexamer sequences) were selected based on the difference in the frequency of hexamers between promoters and non-promoters. In RTPFSGA, random triplet-pairs (RTPs) were selected by exploiting a genetic algorithm that distinguishes frequencies of non-adjacent triplet pairs between promoters and non-promoters. Then, a support vector machine (SVM), a nonlinear machine-learning algorithm, was used to classify promoters and non-promoters by combining these two feature selection approaches. We referred to this novel algorithm as PromoBot. Promoter sequences were collected from the PlantProm database. Non-promoter sequences were collected from plant mRNA, rRNA, and tRNA of PlantGDB and plant miRNA of miRBase. Then, in order to validate the proposed algorithm, we applied a 5-fold cross validation test. Training data sets were used to select features based on FDAFSA and RTPFSGA, and these features were used to train the SVM. We achieved 89% sensitivity and 86% specificity. We compared our PromoBot algorithm to five other algorithms. It was found that the sensitivity and specificity of PromoBot performed well (or even better) with the algorithms tested. These results show that the two proposed feature selection methods based on hexamer frequencies and random triplet-pair could be successfully incorporated into a supervised machine learning method in promoter classification problem. As such, we expect that PromoBot can be used to help identify new plant promoters. Source codes and analysis results of this work could be provided upon request.
Publisher: IEEE
Date: 06-2011
Publisher: ACM
Date: 07-07-2012
Publisher: IEEE
Date: 12-2010
Publisher: Science Publications
Date: 12-2014
Publisher: ACM
Date: 25-06-2005
Publisher: ACM
Date: 15-02-2010
Publisher: Public Library of Science (PLoS)
Date: 12-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Publisher: Elsevier BV
Date: 08-2008
Publisher: IMPERIAL COLLEGE PRESS
Date: 08-2011
DOI: 10.1142/P769
Publisher: Wiley
Date: 05-02-2016
Publisher: American Association for the Advancement of Science (AAAS)
Date: 05-10-2018
Abstract: Repeated emergence, not international dissemination, is behind the rise of multidrug-resistant lineage 4 tuberculosis.
Publisher: ACM
Date: 12-07-2011
Publisher: IEEE
Date: 06-2011
Publisher: IEEE
Date: 06-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2015
Publisher: Springer Science and Business Media LLC
Date: 2010
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Elsevier BV
Date: 06-2021
Publisher: Springer Berlin Heidelberg
Date: 2014
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: WORLD SCIENTIFIC
Date: 09-2007
Publisher: Wiley
Date: 05-02-2016
Publisher: Cold Spring Harbor Laboratory
Date: 08-02-2021
DOI: 10.1101/2021.02.07.21250586
Abstract: 1 Properties of city-level commuting networks are expected to influence epidemic potential of cities and modify the speed and spatial trajectory of epidemics when they occur. In this study, we use aggregated mobile phone user data to reconstruct commuter mobility networks for Bangkok (Thailand) and Dhaka (Bangladesh), two megacities in Asia with populations of 16 and 21 million people, respectively. We model the dynamics of directly-transmitted infections (such as SARS-CoV2) propagating on these commuting networks, and find that differences in network structure between the two cities drive ergent predicted epidemic trajectories: the commuting network in Bangkok is composed of geographically-contiguous modular communities and epidemic dispersal is correlated with geographic distance between locations, whereas the network in Dhaka has less distinct geographic structure and epidemic dispersal is less constrained by geographic distance. We also find that the predicted dynamics of epidemics vary depending on the local topology of the network around the origin of the outbreak. Measuring commuter mobility, and understanding how commuting networks shape epidemic dynamics at the city level, can support surveillance and preparedness efforts in large cities at risk for emerging or imported epidemics.
Publisher: IEEE
Date: 12-2010
Publisher: ACM
Date: 08-07-2006
Publisher: ACM
Date: 12-07-2011
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: IEEE
Date: 06-2010
Publisher: ACM
Date: 12-07-2011
Publisher: IEEE
Date: 12-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2007
Publisher: Springer Science and Business Media LLC
Date: 21-05-2010
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: ACM
Date: 25-06-2017
Publisher: Springer Science and Business Media LLC
Date: 2013
Publisher: Elsevier BV
Date: 08-2015
Publisher: ACM
Date: 08-07-2006
Publisher: Wiley
Date: 05-02-2016
No related grants have been discovered for Nasimul Noman.