The Experts below are selected from a list of 78615 Experts worldwide ranked by ideXlab platform
Safaai Deris - One of the best experts on this subject based on the ideXlab platform.
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A two-stage method to select a smaller subset of informative genes for Cancer Classification
International Journal of Innovative Computing Information and Control, 2009Co-Authors: Mohd Saberi Mohamad, Sigeru Omatu, Michifumi Yoshioka, Safaai DerisAbstract:Gene expression data measured by microarray machines are useful for Cancer Classification. However, it faces with several problems in selecting genes for the Classification due to many irrelevant genes, noisy data, and the availability of a small number of samples compared to a huge number of genes (high-dimensional data). Hence, this paper proposes a two-stage gene selection method to select a smaller (near-optimal) subset of informative genes that is most relevant for the Cancer Classification. It has two stages: 1) pre-selecting genes using a filter method to produce a subset of genes; 2) optimising the gene subset using a multi-objective hybrid method to automatically yield a smaller subset of informative genes. Three gene expression data sets are used to test the effectiveness of the proposed method. Experimental results show that the performance of the proposed method is superior to other experimental methods and related previous works.
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A cyclic hybrid method to select a smaller subset of informative genes for Cancer Classification
International Journal of Innovative Computing Information and Control, 2009Co-Authors: Mohd Saberi Mohamad, Sigeru Omatu, Michifumi Yoshioka, Safaai DerisAbstract:Microarray data are expected to be useful for Cancer Classification. The main problem that needs to be addressed is the selection of a smaller subset of genes from the thousands of genes in the data that contributes to a Cancer disease. This selection process is difficult due to many irrelevant genes, noisy data, and the availability of the small number of samples compared to the huge number of genes (higher-dimensional data). Hence, this paper aims to select a smaller subset of informative genes that is the most relevant for the Cancer Classification. To achieve the aim, a cyclic hybrid method has been proposed. Five real microarray data sets are used to test the effectiveness of the method. Experimental results show that the performance of the proposed method is superior to other experimental methods and related previous works in terms of Classification accuracy and the number of selected genes. In addition, a scatter gene graph and a list of informative genes in the best gene subsets are also presented for biological usage.
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Three-stage method for selecting informative genes for Cancer Classification
IEEJ Transactions on Electrical and Electronic Engineering, 2009Co-Authors: Mohd Saberi Mohamad, Sigeru Omatu, Safaai Deris, Michifumi YoshiokaAbstract:Gene expression data produced by microarray machines are useful for Cancer Classification. However, the process of gene selection for the Classification faces a major problem because of the properties of the data such as the small number of samples compared with the huge number of genes (high-dimensional data), irrelevant genes, and noisy data. Hence, this paper proposes a three-stage method to select a small subset of informative genes which is most relevant for the Cancer Classification. It has three stages: (i) pre-selecting genes using a filter method to produce a subset of genes; (ii) optimizing the gene subset using a multi-objective hybrid method to yield near-optimal subsets of genes; (iii) analyzing the frequency of appearance of each gene in the different near-optimal gene subsets to produce a small (final) subset of informative genes. Five gene expression data sets are used to test the effectiveness of the proposed method. Experimental results show that the performance of the proposed method is superior to other experimental methods and related previous works. A list of informative genes in the final gene subset is also presented for biological usage.
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A new binary particle swarm optimizer to select a smaller subset of genes for leukaemia Cancer Classification
2008Co-Authors: Mohd Saberi Mohamad, Sigeru Omatu, Safaai Deris, Michifuci Yoshioka, Anazida ZainalAbstract:The application of microarray data for Cancer Classification has recently gained in popularity. The main problem that needs to be addressed is the selection of a smaller subset of genes from the thousands of genes in the data that contributes to a disease. This selection process is difficult because of the availability of the small number of samples compared to the huge number of genes, many irrelevant genes, and noisy genes. Therefore, this paper proposes an improved binary particle swarm optimization to select a near-optimal (smaller) subset of informative genes that is relevant for Cancer Classification. Experimental results show that the performance of the proposed method is superior to the experimental method and other related previous works in terms of Classification accuracy and the number of selected genes.
Hong-qiang Wang - One of the best experts on this subject based on the ideXlab platform.
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ICNC - A Markov chain model-based method for Cancer Classification
2012 8th International Conference on Natural Computation, 2012Co-Authors: Hong-qiang WangAbstract:In this paper, we propose a Markov chain model (MCM)-based method for Cancer Classification. By viewing a gene chain extracted from a gene pathway map as a gene Markov chain (GMC), the method can construct a MCM for different Cancer classes. The resulted MCM captures the co-activity pattern of genes in terms of an initial state distribution and a state transition probability matrix. When used for Cancer Classification, the method first calculates the respective probabilities of a test sample belonging to different Cancer classes according to the corresponding MCM, and then predicts the class of the sample as the one with the highest probability. We evaluate the proposed method on the publicly available leukemia dataset and compare it with several conventional methods.
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A neural network-based biomarker association information extraction approach for Cancer Classification
Journal of biomedical informatics, 2009Co-Authors: Hong-qiang Wang, Hau-san Wong, Hailong Zhu, Timothy T. C. YipAbstract:A number of different approaches based on high-throughput data have been developed for Cancer Classification. However, these methods often ignore the underlying correlation between the expression levels of different biomarkers which are related to Cancer. From a biological viewpoint, the modeling of these abnormal associations between biomarkers will play an important role in Cancer Classification. In this paper, we propose an approach based on the concept of Biomarker Association Networks (BAN) for Cancer Classification. The BAN is modeled as a neural network, which can capture the associations between the biomarkers by minimizing an energy function. Based on the BAN, a new Cancer Classification approach is developed. We validate the proposed approach on four publicly available biomarker expression datasets. The derived Biomarker Association Networks are observed to be significantly different for different Cancer classes, which help reveal the underlying deviant biomarker association patterns responsible for different Cancer types. Extensive comparisons show the superior performance of the BAN-based Classification approach over several conventional Classification methods.
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Constructing the gene regulation-level representation of microarray data for Cancer Classification
Journal of biomedical informatics, 2007Co-Authors: Hau-san Wong, Hong-qiang WangAbstract:In this paper, we propose a regulation-level representation for microarray data and optimize it using genetic algorithms (GAs) for Cancer Classification. Compared with the traditional expression-level features, this representation can greatly reduce the dimensionality of microarray data and accommodate noise and variability such that many statistical machine-learning methods now become applicable and efficient for Cancer Classification. Experimental results on real-world microarray datasets show that the regulation-level representation can consistently converge at a solution with three regulation levels. This verifies the existence of the three regulation levels (up-regulation, down-regulation and non-significant regulation) associated with a particular biological phenotype. The ternary regulation-level representation not only improves the Cancer Classification capability but also facilitates the visualization of microarray data.
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Extracting gene regulation information for Cancer Classification
Pattern Recognition, 2007Co-Authors: Hong-qiang Wang, Hau-san Wong, De-shuang Huang, Jun ShuAbstract:In this paper, we address the problem of extracting gene regulation information from microarray data for Cancer Classification. From the biological viewpoint, a model of gene regulation probability is established where three types of gene regulation states in a tissue sample are assumed and then two regulation events correlated with the class distinction are defined. Different from the previous approaches, the proposed algorithm uses gene regulation probabilities as carriers of regulation information to select genes and construct classifiers. The proposed approach is successfully applied to two public available microarray data sets, the leukemia data and the prostate data. Experimental results suggest that gene selection based on regulation information can greatly improve Cancer Classification, and the classifier based on regulation information is more efficient and more stable than several previous Classification algorithms.
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Non-linear Cancer Classification using a modified radial basis function Classification algorithm.
Journal of biomedical science, 2005Co-Authors: Hong-qiang Wang, De-shuang HuangAbstract:This paper proposes a modified radial basis function Classification algorithm for non-linear Cancer Classification. In the algorithm, a modified simulated annealing method is developed and combined with the linear least square and gradient paradigms to optimize the structure of the radial basis function (RBF) classifier. The proposed algorithm can be adopted to perform non-linear Cancer Classification based on gene expression profiles and applied to two microarray data sets involving various human tumor classes: (1) Normal versus colon tumor; (2) acute myeloid leukemia (AML) versus acute lymphoblastic leukemia (ALL). Finally, accuracy and stability for the proposed algorithm are further demonstrated by comparing with the other Cancer Classification algorithms.
Mohd Saberi Mohamad - One of the best experts on this subject based on the ideXlab platform.
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An improved parallelized mRMR for gene subset selection in Cancer Classification
International Journal on Advanced Science Engineering and Information Technology, 2017Co-Authors: Rohani Mohammad Kusairi, Kohbalan Moorthy, Habibollah Haron, Mohd Saberi Mohamad, Suhaimi Napis, Shahreen KasimAbstract:DNA microarray technique has become a more attractive tool for Cancer Classification in the scientific and industrial fields. Based on the previous researchers, the conventional approach for Cancer Classification is primarily based on morphological appearance of the tumor. The limitations of this approach are bias in identify the tumors by expert and faced the difficulty in differentiate the Cancer subtypes due to most Cancers being highly related to the specific biological insight. Thus, this study propose an improved parallelized Minimum Redundancy Maximum Relevance (mRMR), which is a particularly fast feature selection method for finding a set of both relevant and complementary features. The mRMR can identify genes more relevance to biological context that leads to richer biological interpretations. The proposed method is expected to achieve accurate Classification performance using small number of predictive genes when tested using two datasets from Cancer Genome Project and compared to previous methods.
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A two-stage method to select a smaller subset of informative genes for Cancer Classification
International Journal of Innovative Computing Information and Control, 2009Co-Authors: Mohd Saberi Mohamad, Sigeru Omatu, Michifumi Yoshioka, Safaai DerisAbstract:Gene expression data measured by microarray machines are useful for Cancer Classification. However, it faces with several problems in selecting genes for the Classification due to many irrelevant genes, noisy data, and the availability of a small number of samples compared to a huge number of genes (high-dimensional data). Hence, this paper proposes a two-stage gene selection method to select a smaller (near-optimal) subset of informative genes that is most relevant for the Cancer Classification. It has two stages: 1) pre-selecting genes using a filter method to produce a subset of genes; 2) optimising the gene subset using a multi-objective hybrid method to automatically yield a smaller subset of informative genes. Three gene expression data sets are used to test the effectiveness of the proposed method. Experimental results show that the performance of the proposed method is superior to other experimental methods and related previous works.
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A cyclic hybrid method to select a smaller subset of informative genes for Cancer Classification
International Journal of Innovative Computing Information and Control, 2009Co-Authors: Mohd Saberi Mohamad, Sigeru Omatu, Michifumi Yoshioka, Safaai DerisAbstract:Microarray data are expected to be useful for Cancer Classification. The main problem that needs to be addressed is the selection of a smaller subset of genes from the thousands of genes in the data that contributes to a Cancer disease. This selection process is difficult due to many irrelevant genes, noisy data, and the availability of the small number of samples compared to the huge number of genes (higher-dimensional data). Hence, this paper aims to select a smaller subset of informative genes that is the most relevant for the Cancer Classification. To achieve the aim, a cyclic hybrid method has been proposed. Five real microarray data sets are used to test the effectiveness of the method. Experimental results show that the performance of the proposed method is superior to other experimental methods and related previous works in terms of Classification accuracy and the number of selected genes. In addition, a scatter gene graph and a list of informative genes in the best gene subsets are also presented for biological usage.
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Three-stage method for selecting informative genes for Cancer Classification
IEEJ Transactions on Electrical and Electronic Engineering, 2009Co-Authors: Mohd Saberi Mohamad, Sigeru Omatu, Safaai Deris, Michifumi YoshiokaAbstract:Gene expression data produced by microarray machines are useful for Cancer Classification. However, the process of gene selection for the Classification faces a major problem because of the properties of the data such as the small number of samples compared with the huge number of genes (high-dimensional data), irrelevant genes, and noisy data. Hence, this paper proposes a three-stage method to select a small subset of informative genes which is most relevant for the Cancer Classification. It has three stages: (i) pre-selecting genes using a filter method to produce a subset of genes; (ii) optimizing the gene subset using a multi-objective hybrid method to yield near-optimal subsets of genes; (iii) analyzing the frequency of appearance of each gene in the different near-optimal gene subsets to produce a small (final) subset of informative genes. Five gene expression data sets are used to test the effectiveness of the proposed method. Experimental results show that the performance of the proposed method is superior to other experimental methods and related previous works. A list of informative genes in the final gene subset is also presented for biological usage.
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A new binary particle swarm optimizer to select a smaller subset of genes for leukaemia Cancer Classification
2008Co-Authors: Mohd Saberi Mohamad, Sigeru Omatu, Safaai Deris, Michifuci Yoshioka, Anazida ZainalAbstract:The application of microarray data for Cancer Classification has recently gained in popularity. The main problem that needs to be addressed is the selection of a smaller subset of genes from the thousands of genes in the data that contributes to a disease. This selection process is difficult because of the availability of the small number of samples compared to the huge number of genes, many irrelevant genes, and noisy genes. Therefore, this paper proposes an improved binary particle swarm optimization to select a near-optimal (smaller) subset of informative genes that is relevant for Cancer Classification. Experimental results show that the performance of the proposed method is superior to the experimental method and other related previous works in terms of Classification accuracy and the number of selected genes.
Lei Xing - One of the best experts on this subject based on the ideXlab platform.
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Prostate Cancer Classification with multiparametric MRI transfer learning model.
Medical physics, 2019Co-Authors: Yixuan Yuan, Wenjian Qin, Mark K. Buyyounouski, Bulat Ibragimov, S Hancock, Bin Han, Lei XingAbstract:Purpose Prostate Cancer Classification has a significant impact on the prognosis and treatment planning of patients. Currently, this Classification is based on the Gleason score analysis of biopsied tissues, which is neither accurate nor risk free. This study aims to learn discriminative features in prostate images and assist physicians in classifying prostate Cancer automatically. Methods We develop a novel multiparametric magnetic resonance transfer learning (MPTL) method to automatically stage prostate Cancer. We first establish a deep convolutional neural network with three branch architectures, which transfer pretrained model to compute features from multiparametric MRI images (mp-MRI): T2w transaxial, T2w sagittal, and apparent diffusion coefficient (ADC). The learned features are concatenated to represent information of mp-MRI sequences. A new image similarity constraint is then proposed to enable the distribution of the features within the same category in a narrow angle region. With the joint constraints of softmax loss and image similarity loss in the fine-tuning process, the MPTL can provide descriptive features with intraclass compactness and interclass separability. Results Two cohorts: 132 cases from our institutional review board-approved patient database and 112 cases from the PROSTATEx-2 Challenge are utilized to evaluate the robustness and effectiveness of the proposed MPTL model. Our model achieved high accuracy of prostate Cancer Classification (accuracy of 86.92%). Moreover, the comparison results demonstrate that our method outperforms both hand-crafted feature-based methods and existing deep learning models in prostate Cancer Classification with higher accuracy. Conclusion The experiment results showed that the proposed method can learn discriminative features in prostate images and classify the Cancer accurately. Our MPTL model could be further applied in the clinical practice to provide valuable information for Cancer treatment and precision medicine.
Sigeru Omatu - One of the best experts on this subject based on the ideXlab platform.
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A two-stage method to select a smaller subset of informative genes for Cancer Classification
International Journal of Innovative Computing Information and Control, 2009Co-Authors: Mohd Saberi Mohamad, Sigeru Omatu, Michifumi Yoshioka, Safaai DerisAbstract:Gene expression data measured by microarray machines are useful for Cancer Classification. However, it faces with several problems in selecting genes for the Classification due to many irrelevant genes, noisy data, and the availability of a small number of samples compared to a huge number of genes (high-dimensional data). Hence, this paper proposes a two-stage gene selection method to select a smaller (near-optimal) subset of informative genes that is most relevant for the Cancer Classification. It has two stages: 1) pre-selecting genes using a filter method to produce a subset of genes; 2) optimising the gene subset using a multi-objective hybrid method to automatically yield a smaller subset of informative genes. Three gene expression data sets are used to test the effectiveness of the proposed method. Experimental results show that the performance of the proposed method is superior to other experimental methods and related previous works.
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A cyclic hybrid method to select a smaller subset of informative genes for Cancer Classification
International Journal of Innovative Computing Information and Control, 2009Co-Authors: Mohd Saberi Mohamad, Sigeru Omatu, Michifumi Yoshioka, Safaai DerisAbstract:Microarray data are expected to be useful for Cancer Classification. The main problem that needs to be addressed is the selection of a smaller subset of genes from the thousands of genes in the data that contributes to a Cancer disease. This selection process is difficult due to many irrelevant genes, noisy data, and the availability of the small number of samples compared to the huge number of genes (higher-dimensional data). Hence, this paper aims to select a smaller subset of informative genes that is the most relevant for the Cancer Classification. To achieve the aim, a cyclic hybrid method has been proposed. Five real microarray data sets are used to test the effectiveness of the method. Experimental results show that the performance of the proposed method is superior to other experimental methods and related previous works in terms of Classification accuracy and the number of selected genes. In addition, a scatter gene graph and a list of informative genes in the best gene subsets are also presented for biological usage.
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Three-stage method for selecting informative genes for Cancer Classification
IEEJ Transactions on Electrical and Electronic Engineering, 2009Co-Authors: Mohd Saberi Mohamad, Sigeru Omatu, Safaai Deris, Michifumi YoshiokaAbstract:Gene expression data produced by microarray machines are useful for Cancer Classification. However, the process of gene selection for the Classification faces a major problem because of the properties of the data such as the small number of samples compared with the huge number of genes (high-dimensional data), irrelevant genes, and noisy data. Hence, this paper proposes a three-stage method to select a small subset of informative genes which is most relevant for the Cancer Classification. It has three stages: (i) pre-selecting genes using a filter method to produce a subset of genes; (ii) optimizing the gene subset using a multi-objective hybrid method to yield near-optimal subsets of genes; (iii) analyzing the frequency of appearance of each gene in the different near-optimal gene subsets to produce a small (final) subset of informative genes. Five gene expression data sets are used to test the effectiveness of the proposed method. Experimental results show that the performance of the proposed method is superior to other experimental methods and related previous works. A list of informative genes in the final gene subset is also presented for biological usage.
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A new binary particle swarm optimizer to select a smaller subset of genes for leukaemia Cancer Classification
2008Co-Authors: Mohd Saberi Mohamad, Sigeru Omatu, Safaai Deris, Michifuci Yoshioka, Anazida ZainalAbstract:The application of microarray data for Cancer Classification has recently gained in popularity. The main problem that needs to be addressed is the selection of a smaller subset of genes from the thousands of genes in the data that contributes to a disease. This selection process is difficult because of the availability of the small number of samples compared to the huge number of genes, many irrelevant genes, and noisy genes. Therefore, this paper proposes an improved binary particle swarm optimization to select a near-optimal (smaller) subset of informative genes that is relevant for Cancer Classification. Experimental results show that the performance of the proposed method is superior to the experimental method and other related previous works in terms of Classification accuracy and the number of selected genes.
