Clustering-based methods have become popular approach to analysing gene expression profiles and t... more Clustering-based methods have become popular approach to analysing gene expression profiles and to identifying a set of significant genes. However, their conclusions rely on an outcome of a single clustering model. This paper highlights the fact that each model can result in different gene profiles and sets of the most significant genes. Therefore, the result may not be accurate, and most importantly, not reliable. In order to identify a more reliable set of genes, different cluster models were created using fuzzy c-means clustering. Genes were then ranked using SVD-QR-based orthogonal least square method and selected from each cluster model. The results suggest that the genes identified are more reliable and provide more valuable information than those stratified by a single method.
International Journal of Reconfigurable Computing, 2012
K-means clustering has been widely used in processing large datasets in many fields of studies. A... more K-means clustering has been widely used in processing large datasets in many fields of studies. Advancement in many data collection techniques has been generating enormous amounts of data, leaving scientists with the challenging task of processing them. Using General Purpose Processors (GPPs) to process large datasets may take a long time; therefore many acceleration methods have been proposed in the literature to speed up the processing of such large datasets. In this work, a parameterized implementation of the K-means clustering algorithm in Field Programmable Gate Array (FPGA) is presented and compared with previous FPGA implementation as well as recent implementations on Graphics Processing Units (GPUs) and GPPs. The proposed FPGA has higher performance in terms of speedup over previous GPP and GPU implementations (two orders and one order of magnitude, resp.). In addition, the FPGA implementation is more energy efficient than GPP and GPU (615x and 31x, resp.). Furthermore, thre...
International Journal of Reconfigurable Computing, 2012
K-means clustering has been widely used in processing large datasets in many fields of studies. A... more K-means clustering has been widely used in processing large datasets in many fields of studies. Advancement in many data collection techniques has been generating enormous amounts of data, leaving scientists with the challenging task of processing them. Using General Purpose Processors (GPPs) to process large datasets may take a long time; therefore many acceleration methods have been proposed in the literature to speed up the processing of such large datasets. In this work, a parameterized implementation of the K-means clustering algorithm in Field Programmable Gate Array (FPGA) is presented and compared with previous FPGA implementation as well as recent implementations on Graphics Processing Units (GPUs) and GPPs. The proposed FPGA has higher performance in terms of speedup over previous GPP and GPU implementations (two orders and one order of magnitude, resp.). In addition, the FPGA implementation is more energy efficient than GPP and GPU (615x and 31x, resp.). Furthermore, thre...
Engineering Applications of Artificial Intelligence, 2014
Computational annotation of protein functions and structures from sequence features, or predictio... more Computational annotation of protein functions and structures from sequence features, or prediction of certain diseases from gene expression levels are among important applications of computational biology. Developing methods capable of such predictions are not only important in terms of their biological and medical uses but also a very challenging task of pattern recognition due to high input dimensionality and small sample size. Ensemble and multi-view learning has gained popularity due to the rapid rise of such datasets (such as the protein and arrhythmia datasets used in this paper) with large numbers of variables. However, the classical ensemble approach does not take into account conditional interdependences among the views. In this paper, we present a two stage supervised multi-view learning technique called parallel interacting multi-view learning (PIML). In the first stage of PIML, similar to the ensemble method, the views are individually used by a predictor, and the class posterior probability estimates are obtained. In the second stage, each view is trained using its own features along with the class posterior probability estimates of the other views as the summary information of other views. This is a hybrid way of combining the views in which the views influence each other during training using the predictions of others interdependences. PIML is demonstrated and compared with the classical ensemble approach on three real datasets.
Clustering-based methods have become popular approach to analysing gene expression profiles and t... more Clustering-based methods have become popular approach to analysing gene expression profiles and to identifying a set of significant genes. However, their conclusions rely on an outcome of a single clustering model. This paper highlights the fact that each model can result in different gene profiles and sets of the most significant genes. Therefore, the result may not be accurate, and most importantly, not reliable. In order to identify a more reliable set of genes, different cluster models were created using fuzzy c-means clustering. Genes were then ranked using SVD-QR-based orthogonal least square method and selected from each cluster model. The results suggest that the genes identified are more reliable and provide more valuable information than those stratified by a single method.
International Journal of Reconfigurable Computing, 2012
K-means clustering has been widely used in processing large datasets in many fields of studies. A... more K-means clustering has been widely used in processing large datasets in many fields of studies. Advancement in many data collection techniques has been generating enormous amounts of data, leaving scientists with the challenging task of processing them. Using General Purpose Processors (GPPs) to process large datasets may take a long time; therefore many acceleration methods have been proposed in the literature to speed up the processing of such large datasets. In this work, a parameterized implementation of the K-means clustering algorithm in Field Programmable Gate Array (FPGA) is presented and compared with previous FPGA implementation as well as recent implementations on Graphics Processing Units (GPUs) and GPPs. The proposed FPGA has higher performance in terms of speedup over previous GPP and GPU implementations (two orders and one order of magnitude, resp.). In addition, the FPGA implementation is more energy efficient than GPP and GPU (615x and 31x, resp.). Furthermore, thre...
International Journal of Reconfigurable Computing, 2012
K-means clustering has been widely used in processing large datasets in many fields of studies. A... more K-means clustering has been widely used in processing large datasets in many fields of studies. Advancement in many data collection techniques has been generating enormous amounts of data, leaving scientists with the challenging task of processing them. Using General Purpose Processors (GPPs) to process large datasets may take a long time; therefore many acceleration methods have been proposed in the literature to speed up the processing of such large datasets. In this work, a parameterized implementation of the K-means clustering algorithm in Field Programmable Gate Array (FPGA) is presented and compared with previous FPGA implementation as well as recent implementations on Graphics Processing Units (GPUs) and GPPs. The proposed FPGA has higher performance in terms of speedup over previous GPP and GPU implementations (two orders and one order of magnitude, resp.). In addition, the FPGA implementation is more energy efficient than GPP and GPU (615x and 31x, resp.). Furthermore, thre...
Engineering Applications of Artificial Intelligence, 2014
Computational annotation of protein functions and structures from sequence features, or predictio... more Computational annotation of protein functions and structures from sequence features, or prediction of certain diseases from gene expression levels are among important applications of computational biology. Developing methods capable of such predictions are not only important in terms of their biological and medical uses but also a very challenging task of pattern recognition due to high input dimensionality and small sample size. Ensemble and multi-view learning has gained popularity due to the rapid rise of such datasets (such as the protein and arrhythmia datasets used in this paper) with large numbers of variables. However, the classical ensemble approach does not take into account conditional interdependences among the views. In this paper, we present a two stage supervised multi-view learning technique called parallel interacting multi-view learning (PIML). In the first stage of PIML, similar to the ensemble method, the views are individually used by a predictor, and the class posterior probability estimates are obtained. In the second stage, each view is trained using its own features along with the class posterior probability estimates of the other views as the summary information of other views. This is a hybrid way of combining the views in which the views influence each other during training using the predictions of others interdependences. PIML is demonstrated and compared with the classical ensemble approach on three real datasets.
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Papers by Huseyin Seker