Afnizanfaizal Abdullah | Universiti Teknologi Malaysia - UTM (original) (raw)
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Papers by Afnizanfaizal Abdullah
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2013
Visualization of cerebrospinal fluid (CSF), that flow in the brain and spinal cord, plays an impo... more Visualization of cerebrospinal fluid (CSF), that flow in the brain and spinal cord, plays an important role to detect neurodegenerative diseases such as Alzheimer's disease. This is performed by measuring the substantial changes in the CSF flow dynamics, volume and/or pressure gradient. Magnetic resonance imaging (MRI) technique has become a prominent tool to quantitatively measure these changes and image segmentation method has been widely used to distinguish the CSF flows from the brain tissues. However, this is often hampered by the presence of partial volume effect in the images. In this paper, a new hybrid evolutionary spatial fuzzy clustering method is introduced to overcome the partial volume effect in the MRI images. The proposed method incorporates Expectation Maximization (EM) method, which is improved by the evolutionary operations of the Genetic Algorithm (GA) to differentiate the CSF from the brain tissues. The proposed improvement is incorporated into a spatial-based fuzzy clustering (SFCM) method to improve segmentation of the boundary curve of the CSF and the brain tissues. The proposed method was validated using MRI images of Alzheimer's disease patient. The results presented that the proposed method is capable to filter the CSF regions from the brain tissues more effectively compared to the standard EM, FCM, and SFCM methods.
PloS one, 2013
The development of accurate computational models of biological processes is fundamental to comput... more The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test. (SNVA) PLOS ONE | www.plosone.org
PloS one, 2013
One of the key aspects of computational systems biology is the investigation on the dynamic biolo... more One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This study is hoped to provide a new insight in developing more accurate and reliable biological models based on limited and low quality experimental data. (MSM) PLOS ONE | www.plosone.org
Particle Swarm Optimization (PSO) has achieved remarkable attentions for its capability to solve ... more Particle Swarm Optimization (PSO) has achieved remarkable attentions for its capability to solve diverse global optimization problems. However, this method also shows several limitations. PSO easily trapped in the global optimum and often required vast computational cost when solving high dimensional problems. Therefore, we propose some modifications to overcome these issues. In this work, Differential Evolution (DE) mutation and crossover operations are implemented to improve local best particles searching in PSO. A numerical analysis is carried out using benchmark functions and is compared with standard PSO and DE method. Results presented suggest the prospective of our proposed method.
Study on topology structure of protein interaction network has been suggested as a potential effo... more Study on topology structure of protein interaction network has been suggested as a potential effort to discover biological functions and cellular mechanisms at systems level. In this work, we introduced a graph partitioning method to partition protein interaction network into several clusters of interacting proteins that share similar functions called functional modules. Our proposed method encompasses three major steps which are preprocessing, informative proteins selection and graph partitioning algorithm. We utilized the protein-protein interaction dataset from MIPS to test the proposed method. We use gene ontology information to validate the biological significance of the detected modules. We also downloaded protein complex information to evaluate the performance of our method. In our analysis, the method showed high accuracy performance indicates that this method capable to detect highly significance modules. Hence, this showed that functional modules detected by the proposed method are biologically significant which can be used to predict uncharacterized proteins and infer new complexes.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2013
Visualization of cerebrospinal fluid (CSF), that flow in the brain and spinal cord, plays an impo... more Visualization of cerebrospinal fluid (CSF), that flow in the brain and spinal cord, plays an important role to detect neurodegenerative diseases such as Alzheimer's disease. This is performed by measuring the substantial changes in the CSF flow dynamics, volume and/or pressure gradient. Magnetic resonance imaging (MRI) technique has become a prominent tool to quantitatively measure these changes and image segmentation method has been widely used to distinguish the CSF flows from the brain tissues. However, this is often hampered by the presence of partial volume effect in the images. In this paper, a new hybrid evolutionary spatial fuzzy clustering method is introduced to overcome the partial volume effect in the MRI images. The proposed method incorporates Expectation Maximization (EM) method, which is improved by the evolutionary operations of the Genetic Algorithm (GA) to differentiate the CSF from the brain tissues. The proposed improvement is incorporated into a spatial-based fuzzy clustering (SFCM) method to improve segmentation of the boundary curve of the CSF and the brain tissues. The proposed method was validated using MRI images of Alzheimer's disease patient. The results presented that the proposed method is capable to filter the CSF regions from the brain tissues more effectively compared to the standard EM, FCM, and SFCM methods.
PloS one, 2013
The development of accurate computational models of biological processes is fundamental to comput... more The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test. (SNVA) PLOS ONE | www.plosone.org
PloS one, 2013
One of the key aspects of computational systems biology is the investigation on the dynamic biolo... more One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This study is hoped to provide a new insight in developing more accurate and reliable biological models based on limited and low quality experimental data. (MSM) PLOS ONE | www.plosone.org
Particle Swarm Optimization (PSO) has achieved remarkable attentions for its capability to solve ... more Particle Swarm Optimization (PSO) has achieved remarkable attentions for its capability to solve diverse global optimization problems. However, this method also shows several limitations. PSO easily trapped in the global optimum and often required vast computational cost when solving high dimensional problems. Therefore, we propose some modifications to overcome these issues. In this work, Differential Evolution (DE) mutation and crossover operations are implemented to improve local best particles searching in PSO. A numerical analysis is carried out using benchmark functions and is compared with standard PSO and DE method. Results presented suggest the prospective of our proposed method.
Study on topology structure of protein interaction network has been suggested as a potential effo... more Study on topology structure of protein interaction network has been suggested as a potential effort to discover biological functions and cellular mechanisms at systems level. In this work, we introduced a graph partitioning method to partition protein interaction network into several clusters of interacting proteins that share similar functions called functional modules. Our proposed method encompasses three major steps which are preprocessing, informative proteins selection and graph partitioning algorithm. We utilized the protein-protein interaction dataset from MIPS to test the proposed method. We use gene ontology information to validate the biological significance of the detected modules. We also downloaded protein complex information to evaluate the performance of our method. In our analysis, the method showed high accuracy performance indicates that this method capable to detect highly significance modules. Hence, this showed that functional modules detected by the proposed method are biologically significant which can be used to predict uncharacterized proteins and infer new complexes.