Profile Image

Dr Nasimul Noman

Lecturer

School of Elect Engineering and Computer Science

Career Summary

Biography

Nasimul Noman received his PhD degree in Frontier Informatics from the Graduate School of Frontier Sciences, University of Tokyo in 2007. He served in the Department of Computer Science and Engineering, University of Dhaka as a faculty member. He was a postdoctoral researcher in the Graduate School of Engineering, University of Tokyo with JSPS fellowship. He was working as a research fellow in the Graduate School of Information Since and Technology in University of Tokyo before he joined the University of Newcastle in 2013.

His current research focus is the application of evolutionary computation for synthesis of gene networks for systems and synthetic biology. His research interests also include development of evolutionary algorithms and their application in computational biology and bioinformatics. 

Research Expertise
My major research area is modeling, simulation, reconstruction and optimization of biological networks. I have been working with different types of deterministic and stochastic modeling, simulation and analysis of genetic networks for systems and synthetic biology. I have worked in model based reconstruction of genetic networks from gene expression data for quite long. Evolutionary algorithms are my preferred method for optimization and in silico construction of biological networks. I am fascinated by the field of Evolutionary Computation. My favorite topics are Differential Evolution, Local Search, Memetic Algorithms, Adaptive Evolutionary Algorithms and Genetic Based Machine Learning (GMBL). I have been working for long for development, analysis and application of EC. To me the application of evolutionary algorithms for solving different real world problems, particularly in computational biology and bioinformatics, is very challenging and appealing. I have active research experience in some bioinformatics research topics. The topics of bioinformatics I am interested/experienced in are protein phosphorylation and glycosylation site detection, promoter site prediction, protein secondary structure prediction, regulatory motif detection, etc. I have been working for enhancement, extension and effective utilization of machine learning algorithms for solving different problems in bioinformatics and computational biology.

Teaching Expertise
The courses I am teaching this year are: > Formal Languages and Automata > Operating Systems > Internet Communication In my previous appointments, I taught many undergraduate and graduate courses of Computer Science and Engineering. Some of the courses I taught recently are as follows: • Introduction to Bioinformatics • Compiler Design • Introduction to Circuit Analysis • Microprocessor and Assembly Language • Data Structure

Administrative Expertise
In my appointment in University of Dhaka, I have served as student adviser. Besides, I have worked in different administrative committees such as course curriculum development committee, examination coordination committee etc.

Collaborations
My current research collaborators are 1. Prof. Hitoshi Iba, University of Tokyo. 2. A/Prof. Yannic Rondelez, University of Tokyo.

Qualifications

  • PhD, University of Tokyo - Japan
  • Bachelor of Science (Computer Science), University of Dhaka - Bangladesh
  • Master of Science (Computer Science), University of Dhaka - Bangladesh

Keywords

  • Bioinformatics
  • Computational Biology
  • Computer Algorithms
  • Data Structure
  • Evolutionary Algorithms
  • Evolutionary Computation
  • Synthetic Biology
  • Systems Biology
  • Theory of Computation

Languages

  • Bengali (Fluent)

Fields of Research

Code Description Percentage
060102 Bioinformatics 20
060114 Systems Biology 40
080108 Neural, Evolutionary and Fuzzy Computation 40

Professional Experience

UON Appointment

Title Organisation / Department
Lecturer University of Newcastle
School of Elect Engineering and Computer Science
Australia

Academic appointment

Dates Title Organisation / Department
1/09/2012 - 1/02/2013 Visiting Research Fellow Harvard Medical School
Systems Biology
United States
1/03/2012 - 1/11/2013 Research Fellow The University of Tokyo
School of Information Science and Technology
Japan
1/01/2012 - 1/05/2012 Associate Professor University of Dhaka
Computer Science and Engineering
Bangladesh
1/11/2009 - 1/11/2011 Post Doctoral Researcher The University of Tokyo
School of Engineering
Japan
1/03/2007 - 1/01/2012 Assistant Professor University of Dhaka
Computer Science and Engineering
Bangladesh
1/03/2002 - 1/03/2007 Lecturer University of Dhaka
Computer Science and Engineering
Bangladesh
Edit

Publications

For publications that are currently unpublished or in-press, details are shown in italics.


Book (2 outputs)

Year Citation Altmetrics Link
2016 Iba H, Noman N, Evolutionary Computation in Gene Regulatory Network Research, John Wiley & Sons, 432 (2016)
2011 Iba H, Noman N, New Frontier in Evolutionary Algorithms, World Scientific Publishing Company, London, 304 (2011) [A2]

Chapter (4 outputs)

Year Citation Altmetrics Link
2016 Dinh QH, Aubert N, Noman N, Iba H, Rondelez Y, 'Evolving GRN-Inspired In Vitro Oscillatory Systems', Evolutionary Computation in Gene Regulatory Network Research, John Wiley & Sons, New Jersy (2016) [B1]
2016 Noman N, Iba H, 'A Brief Introduction To Evolutionary And Other Nature-Inspired Algorithms', Evolutionary Computation in Gene Regulatory Network Research, John Wiley & Sons, New Jersey (2016)
2014 Noman N, Palafox L, Iba H, 'Inferring Genetic Networks with a Recurrent Neural Network Model Using Differential Evolution', Springer Handbook of Bio-/Neuroinformatics, Springer, Berlin Heidelberg 355-373 (2014)
DOI 10.1007/978-3-642-30574-0_22
2007 Noman N, Iba H, 'Inferring Regulations in a Genomic Network from Gene Expression Profiles.', Analysis of Biological Data: A Soft Computing Approach, World Scientific 205-229 (2007)
DOI 10.1142/9789812708892_0009
Show 1 more chapter

Journal article (15 outputs)

Year Citation Altmetrics Link
2016 Haque MN, Noman N, Berretta R, Moscato P, 'Heterogeneous ensemble combination search using genetic algorithm for class imbalanced data classification', PLoS ONE, 11 (2016) [C1]

© 2016 Haque et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and repr... [more]

© 2016 Haque et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Classification of datasets with imbalanced sample distributions has always been a challenge. In general, a popular approach for enhancing classification performance is the construction of an ensemble of classifiers. However, the performance of an ensemble is dependent on the choice of constituent base classifiers. Therefore, we propose a genetic algorithm-based search method for finding the optimum combination from a pool of base classifiers to form a heterogeneous ensemble. The algorithm, called GA-EoC, utilises 10 fold-cross validation on training data for evaluating the quality of each candidate ensembles. In order to combine the base classifiers decision into ensemble's output, we used the simple and widely used majority voting approach. The proposed algorithm, along with the random sub-sampling approach to balance the class distribution, has been used for classifying class-imbalanced datasets. Additionally, if a feature set was not available, we used the (a, ß) - k Feature Set method to select a better subset of features for classification. We have tested GA-EoC with three benchmarking datasets from the UCI-Machine Learning repository, one Alzheimer's disease dataset and a subset of the PubFig database of Columbia University. In general, the performance of the proposed method on the chosen datasets is robust and better than that of the constituent base classifiers and many other well-known ensembles. Based on our empirical study we claim that a genetic algorithm is a superior and reliable approach to heterogeneous ensemble construction and we expect that the proposed GA-EoC would perform consistently in other cases.

DOI 10.1371/journal.pone.0146116
Co-authors Pablo Moscato, Regina Berretta, Mohammad Haque
2015 Noman N, Monjo T, Moscato P, Iba H, 'Evolving robust gene regulatory networks.', PLoS One, 10 e0116258 (2015) [C1]
DOI 10.1371/journal.pone.0116258
Citations Scopus - 5Web of Science - 1
Co-authors Pablo Moscato
2015 Peng Y, Hasegawa Y, Noman N, Iba H, 'Temperature compensation via cooperative stability in protein degradation', PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 431 109-123 (2015) [C1]
DOI 10.1016/j.physa.2015.03.002
2015 Dinh HQ, Aubert N, Noman N, Fujii T, Rondelez Y, Iba H, 'An effective method for evolving reaction networks in synthetic biochemical systems', IEEE Transactions on Evolutionary Computation, 19 374-386 (2015) [C1]

© 2014 IEEE.In this paper, we introduce our approach for evolving reaction networks. It is an efficient derivative of the neuroevolution of augmenting topologies algorithm direct... [more]

© 2014 IEEE.In this paper, we introduce our approach for evolving reaction networks. It is an efficient derivative of the neuroevolution of augmenting topologies algorithm directed at the evolution of biochemical systems or molecular programs. Our method addresses the problem of meaningful crossovers between two chemical reaction networks of different topologies. It also builds on features such as speciation to speed up the search, to the point where it can deal with complete, realistic mathematical models of the biochemical processes. We demonstrate this framework by evolving credible biochemical answers to challenging autonomous molecular problems: in vitro batch oscillatory networks that match specific oscillation shapes. Our experimental results suggest that the search space is efficiently covered and that, by using crossover and preserving topological innovations, significant improvements in performance can be obtained for the automatic design of molecular programs.

DOI 10.1109/TEVC.2014.2326863
Citations Scopus - 2
2014 Dinh QH, Noman N, Iba H, 'Oscillatory Synthetic Biological System Construction Using Interactive Evolutionary Computations', Journal of Computer Science, 10 2640-2652 (2014) [C1]
DOI 10.3844/jcssp.2014.2640.2652
2013 Noman N, Palafox L, Iba H, 'Evolving Genetic Networks for Synthetic Biology', NEW GENERATION COMPUTING, 31 71-88 (2013) [C1]
DOI 10.1007/s00354-013-0201-8
Citations Scopus - 3Web of Science - 1
2013 Palafox L, Noman N, Iba H, 'Reverse Engineering of Gene Regulatory Networks Using Dissipative Particle Swarm Optimization', IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 17 577-587 (2013) [C1]
DOI 10.1109/TEVC.2012.2218610
Citations Scopus - 12Web of Science - 10
2012 Ibrahim M, Noman N, Iba H, 'Finding Perfect and Imperfect Biclusters from Gene Expression Data: A Heuristic and A Metaheuristic Approach.', International Journal of Applied Chemistry, 8 (2012)
2011 Azad AKM, Shahid S, Noman N, Lee H, 'Prediction of plant promoters based on hexamers and random triplet pair analysis', ALGORITHMS FOR MOLECULAR BIOLOGY, 6 (2011) [C1]
DOI 10.1186/1748-7188-6-19
Citations Scopus - 3Web of Science - 2
2011 Noman N, Iba H, 'e Constrained differential evolution for economic dispatch with valve-point effect', International Journal of Bio-Inspired Computation, 3 346-357 (2011) [C1]
DOI 10.1504/IJBIC.2011.043607
Citations Scopus - 7Web of Science - 5
2010 Kabir M, Noman N, Iba H, 'Reverse engineering gene regulatory network from microarray data using linear time-variant model', BMC BIOINFORMATICS, 11 (2010) [C1]
DOI 10.1186/1471-2105-11-S1-S56
Citations Scopus - 38Web of Science - 25
2010 Biswas AK, Noman N, Sikder AR, 'Machine learning approach to predict protein phosphorylation sites by incorporating evolutionary information', BMC BIOINFORMATICS, 11 (2010) [C1]
DOI 10.1186/1471-2105-11-273
Citations Scopus - 35Web of Science - 29
2008 Noman N, Iba H, 'Accelerating differential evolution using an adaptive local search', IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 12 107-125 (2008) [C2]
DOI 10.1109/TEVC.2007.895272
Citations Scopus - 367Web of Science - 257
2008 Noman N, Iba H, 'Differential evolution for economic load dispatch problems', ELECTRIC POWER SYSTEMS RESEARCH, 78 1322-1331 (2008) [C1]
DOI 10.1016/j.epsr.2007.11.007
Citations Scopus - 205Web of Science - 152
2007 Noman N, Iba H, 'Inferring gene regulatory networks using differential evolution with local search heuristics', IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 4 634-647 (2007)
DOI 10.1109/TCBB.2007.1058
Citations Scopus - 76Web of Science - 64
Show 12 more journal articles

Conference (26 outputs)

Year Citation Altmetrics Link
2016 Haque MN, Noman N, Berretta R, Moscato P, 'Optimising weights for heterogeneous ensemble of classifiers with differential evolution', Evolutionary Computation (CEC), 2016 IEEE Congress on (2016)
DOI 10.1109/CEC.2016.7743800
Co-authors Mohammad Haque, Regina Berretta, Pablo Moscato
2013 Palafox LF, Noman N, Iba H, 'Extending Population Based Incremental Learning using Dirichlet Processes', 2013 IEEE Congress on Evolutionary Computation, CEC 2013 (2013) [E1]
DOI 10.1109/CEC.2013.6557764
Citations Scopus - 2Web of Science - 1
2013 Hettiarachchi DS, Noman N, Iba H, 'Messy Genetic Algorithm for evolving mathematical function evaluating variable length gene regulatory networks', 2013 IEEE Congress on Evolutionary Computation, CEC 2013 (2013) [E1]
DOI 10.1109/CEC.2013.6557824
Citations Scopus - 1Web of Science - 1
2012 Komiya K, Noman N, Iba H, 'The search for robust topologies of oscillatory gene regulatory networks by evolutionary computation', GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion (2012) [E1]
DOI 10.1145/2330784.2330963
Citations Scopus - 2
2012 He C, Noman N, Iba H, 'An improved artificial bee colony algorithm with non-separable operator', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2012) [E1]
DOI 10.1007/978-3-642-32645-5_26
2012 Noman N, Palafox L, Iba H, 'On model selection criteria in reverse engineering gene networks using RNN model', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2012) [E1]
DOI 10.1007/978-3-642-32645-5_20
Citations Scopus - 2
2011 Noman N, Iba H, 'Solving dynamic economic dispatch problems using cellular differential evolution', 2011 IEEE Congress of Evolutionary Computation, CEC 2011 (2011) [E1]
DOI 10.1109/CEC.2011.5949947
Citations Scopus - 1Web of Science - 1
2011 Noman N, Bollegala D, Iba H, 'An adaptive differential evolution algorithm', 2011 IEEE Congress of Evolutionary Computation, CEC 2011 (2011) [E1]
DOI 10.1109/CEC.2011.5949891
Citations Scopus - 9Web of Science - 7
2011 Bollegala D, Noman N, Iba H, 'RankDE: Learning a ranking function for information retrieval using differential evolution', Genetic and Evolutionary Computation Conference, GECCO'11 (2011) [E1]
DOI 10.1145/2001576.2001814
Citations Scopus - 3Web of Science - 2
2011 Noman N, Bollegala D, Iba H, 'Differential evolution with self adaptive local search', Genetic and Evolutionary Computation Conference, GECCO'11 (2011) [E1]
DOI 10.1145/2001576.2001725
Citations Scopus - 2
2011 Vatanutanon J, Noman N, Iba H, 'Polynomial selection scheme with dynamic parameter estimation in cellular genetic algorithm', Genetic and Evolutionary Computation Conference, GECCO'11 (2011) [E1]
DOI 10.1145/2001576.2001734
Citations Scopus - 2
2010 Ibrahim M, Noman N, Iba H, 'On the complexity and completeness of robust biclustering algorithm (ROBA)', 2010 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010 (2010) [E1]
DOI 10.1109/ICBBE.2010.5518207
2010 Noman N, Vatanutanon J, Iba H, 'Tuning selection pressure in differential evolution using local selection', Proceedings - 2010 2nd World Congress on Nature and Biologically Inspired Computing, NaBIC 2010 (2010) [E1]
DOI 10.1109/NABIC.2010.5716337
Citations Scopus - 1
2010 Vatanutanon J, Noman N, Iba H, 'Polynomial selection: A new way to tune selective pressure', Proceedings - 2010 2nd World Congress on Nature and Biologically Inspired Computing, NaBIC 2010 (2010) [E1]
DOI 10.1109/NABIC.2010.5716313
Citations Scopus - 1
2010 Mondal BS, Sarkar AK, Hasan MM, Noman N, 'Reconstruction of gene regulatory networks using differential evolution', Proceedings of 2010 13th International Conference on Computer and Information Technology, ICCIT 2010 (2010) [E1]
DOI 10.1109/ICCITECHN.2010.5723898
Citations Scopus - 2
2010 Hasan MM, Noman N, Iba H, 'A prior knowledge based approach to infer gene regulatory networks', ISB 2010 Proceedings - International Symposium on Biocomputing (2010) [E1]
DOI 10.1145/1722024.1722069
2010 Noman N, Iba H, 'Cellular differential evolution algorithm', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2010) [E1]
DOI 10.1007/978-3-642-17432-2_30
Citations Scopus - 1Web of Science - 3
2010 Ishiwata H, Noman N, Iba H, 'Emergence of cooperation in a bio-inspired multi-agent system', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2010) [E1]
DOI 10.1007/978-3-642-17432-2_37
Citations Web of Science - 1
2006 Noman N, Iba H, 'On the reconstruction of gene regulatory networks from noisy expression profiles', 2006 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-6 (2006)
2006 Noman N, Iba H, 'Inference of genetic networks using S-system: Information criteria for model selection', GECCO 2006: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOL 1 AND 2 (2006)
Citations Scopus - 9Web of Science - 10
2006 Noman N, Lba H, 'A new generation alternation model for differential evolution', GECCO 2006: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOL 1 AND 2 (2006)
Citations Scopus - 16Web of Science - 5
2006 Noman N, Iba H, 'On the reconstruction of gene regulatory networks from noisy expression profiles', 2006 IEEE Congress on Evolutionary Computation, CEC 2006 (2006)

Noise is inevitable in microarray data. The real challenge lies in identifying the biomolecular interactions in spite of the significant noise level present in the expression prof... [more]

Noise is inevitable in microarray data. The real challenge lies in identifying the biomolecular interactions in spite of the significant noise level present in the expression proflies that current technology offers. In this paper, we study the usefulness of an evolutionary approach in reverse engineering the biomolecular connections in a gene circuit from observed system dynamics that is contaminated with noise. The method uses an Information Criteria based fitness evaluation for selecting models, represented in decoupled S-system formalism, instead of the conventional Mean Squared Error (MSE) based fitness evaluation. The suitability of the method is tested in experiments of reconstructing an artificial network from gene expression profiles with varying noise levels. The proposed fitness function has been found more suitable for identifying correct network topology and for estimating the accurate parameter values compared to the existing one. © 2006 IEEE.

Citations Scopus - 5
2005 Noman N, Iba H, 'Inference of gene regulatory networks using S-system and differential evolution', GECCO 2005: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOLS 1 AND 2 (2005)
Citations Scopus - 24Web of Science - 16
2005 Noman N, Iba H, 'Enhancing Differential Evolution performance with local search for high dimensional function optimization', GECCO 2005: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOLS 1 AND 2 (2005)
Citations Scopus - 45Web of Science - 37
2005 Noman N, Iba H, 'Reverse engineering genetic networks using evolutionary computation.', Genome informatics. International Conference on Genome Informatics. (2005)

This paper proposes an improved evolutionary method for constructing the underlying network structure and inferring effective kinetic parameters from the time series data of gene ... [more]

This paper proposes an improved evolutionary method for constructing the underlying network structure and inferring effective kinetic parameters from the time series data of gene expression using decoupled S-system formalism. We employed Trigonometric Differential Evolution (TDE) as the optimization engine of our algorithm for capturing the dynamics in gene expression data. A more effective fitness function for attaining the sparse structure, which is the hallmark of biological networks, has been applied. Experiments on artificial genetic network show the power of the algorithm in constructing the network structure and predicting the regulatory parameters. The method is used to evaluate interactions between genes in the SOS signaling pathway in Escherichia coli using gene expression data.

Citations Scopus - 38
2004 Noman N, Okada K, Hosoyama N, Iba H, 'Use of clustering to improve the layout of gene network for visualization', CEC2004: PROCEEDINGS OF THE 2004 CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1 AND 2 (2004)
Show 23 more conferences
Edit

Grants and Funding

Summary

Number of grants 4
Total funding $21,200

Click on a grant title below to expand the full details for that specific grant.


20161 grants / $10,000

Multi-objective Memetic Algorithm for Large-Scale Community Detection Considering both Topology and Contents$10,000

Funding body: Faculty of Engineering and Built Environment - The University of Newcastle (Australia)

Funding body Faculty of Engineering and Built Environment - The University of Newcastle (Australia)
Project Team

A/Prof. Regina Berretta, Dr. Nasimul Noman, Prof. Pablo Moscato and Dr. Luke Mathieson

Scheme FEBE Strategic Pilot Grant
Role Investigator
Funding Start 2016
Funding Finish 2016
GNo
Type Of Funding Internal
Category INTE
UON N

20151 grants / $5,000

A Multi-Objective Evolutionary Algorithm for Breast Cancer Patient Stratification$5,000

Funding body: Faculty of Engineering and Built Environment - The University of Newcastle (Australia)

Funding body Faculty of Engineering and Built Environment - The University of Newcastle (Australia)
Scheme FEBE Strategic Pilot Grant
Role Lead
Funding Start 2015
Funding Finish 2015
GNo
Type Of Funding Internal
Category INTE
UON N

20142 grants / $6,200

New Staff Grant$5,000

Funding body: University of Newcastle - Faculty of Engineering & Built Environment

Funding body University of Newcastle - Faculty of Engineering & Built Environment
Project Team Doctor Nasimul Noman
Scheme New Staff Grant
Role Lead
Funding Start 2014
Funding Finish 2014
GNo G1400989
Type Of Funding Internal
Category INTE
UON Y

15th International Conference on Systems Biology, Melbourne, 14-18 September, 2014$1,200

Funding body: University of Newcastle - Faculty of Engineering & Built Environment

Funding body University of Newcastle - Faculty of Engineering & Built Environment
Project Team Doctor Nasimul Noman
Scheme Travel Grant
Role Lead
Funding Start 2014
Funding Finish 2015
GNo G1400795
Type Of Funding Internal
Category INTE
UON Y
Edit

Research Supervision

Number of supervisions

Completed0
Current2

Total current UON EFTSL

PhD0.2

Current Supervision

Commenced Level of Study Research Title / Program / Supervisor Type
2014 PhD Improving generalisation in structure-learning of boolean networks
PhD (Computer Science), Faculty of Engineering and Built Environment, The University of Newcastle
Co-Supervisor
2013 PhD A New Feature Selection Approach Based on Minimum Spanning Tree Using Evolutionary Computation
Computer Science, Faculty of Engineering and Built Environment - The University of Newcastle (Australia)
Co-Supervisor
Edit

Dr Nasimul Noman

Position

Lecturer
School of Elect Engineering and Computer Science
Faculty of Engineering and Built Environment

Contact Details

Email nasimul.noman@newcastle.edu.au
Phone (02) 4042 0488

Office

Room ES228
Building ES Building
Location Callaghan
University Drive
Callaghan, NSW 2308
Australia
Edit