The Experts below are selected from a list of 90318 Experts worldwide ranked by ideXlab platform
Michael R Kosorok - One of the best experts on this subject based on the ideXlab platform.
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Detection of gene pathways with Predictive Power for breast cancer prognosis
BMC Bioinformatics, 2010Co-Authors: Shuangge Ma, Michael R KosorokAbstract:Background Prognosis is of critical interest in breast cancer research. Biomedical studies suggest that genomic measurements may have independent Predictive Power for prognosis. Gene profiling studies have been conducted to search for Predictive genomic measurements. Genes have the inherent pathway structure, where pathways are composed of multiple genes with coordinated functions. The goal of this study is to identify gene pathways with Predictive Power for breast cancer prognosis. Since our goal is fundamentally different from that of existing studies, a new pathway analysis method is proposed. Results The new method advances beyond existing alternatives along the following aspects. First, it can assess the Predictive Power of gene pathways, whereas existing methods tend to focus on model fitting accuracy only. Second, it can account for the joint effects of multiple genes in a pathway, whereas existing methods tend to focus on the marginal effects of genes. Third, it can accommodate multiple heterogeneous datasets, whereas existing methods analyze a single dataset only. We analyze four breast cancer prognosis studies and identify 97 pathways with significant Predictive Power for prognosis. Important pathways missed by alternative methods are identified. Conclusions The proposed method provides a useful alternative to existing pathway analysis methods. Identified pathways can provide further insights into breast cancer prognosis.
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detection of gene pathways with Predictive Power for breast cancer prognosis
BMC Bioinformatics, 2010Co-Authors: Michael R KosorokAbstract:Prognosis is of critical interest in breast cancer research. Biomedical studies suggest that genomic measurements may have independent Predictive Power for prognosis. Gene profiling studies have been conducted to search for Predictive genomic measurements. Genes have the inherent pathway structure, where pathways are composed of multiple genes with coordinated functions. The goal of this study is to identify gene pathways with Predictive Power for breast cancer prognosis. Since our goal is fundamentally different from that of existing studies, a new pathway analysis method is proposed. The new method advances beyond existing alternatives along the following aspects. First, it can assess the Predictive Power of gene pathways, whereas existing methods tend to focus on model fitting accuracy only. Second, it can account for the joint effects of multiple genes in a pathway, whereas existing methods tend to focus on the marginal effects of genes. Third, it can accommodate multiple heterogeneous datasets, whereas existing methods analyze a single dataset only. We analyze four breast cancer prognosis studies and identify 97 pathways with significant Predictive Power for prognosis. Important pathways missed by alternative methods are identified. The proposed method provides a useful alternative to existing pathway analysis methods. Identified pathways can provide further insights into breast cancer prognosis.
Shuangge Ma - One of the best experts on this subject based on the ideXlab platform.
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Detection of gene pathways with Predictive Power for breast cancer prognosis
BMC Bioinformatics, 2010Co-Authors: Shuangge Ma, Michael R KosorokAbstract:Background Prognosis is of critical interest in breast cancer research. Biomedical studies suggest that genomic measurements may have independent Predictive Power for prognosis. Gene profiling studies have been conducted to search for Predictive genomic measurements. Genes have the inherent pathway structure, where pathways are composed of multiple genes with coordinated functions. The goal of this study is to identify gene pathways with Predictive Power for breast cancer prognosis. Since our goal is fundamentally different from that of existing studies, a new pathway analysis method is proposed. Results The new method advances beyond existing alternatives along the following aspects. First, it can assess the Predictive Power of gene pathways, whereas existing methods tend to focus on model fitting accuracy only. Second, it can account for the joint effects of multiple genes in a pathway, whereas existing methods tend to focus on the marginal effects of genes. Third, it can accommodate multiple heterogeneous datasets, whereas existing methods analyze a single dataset only. We analyze four breast cancer prognosis studies and identify 97 pathways with significant Predictive Power for prognosis. Important pathways missed by alternative methods are identified. Conclusions The proposed method provides a useful alternative to existing pathway analysis methods. Identified pathways can provide further insights into breast cancer prognosis.
Michael D. Schreiber - One of the best experts on this subject based on the ideXlab platform.
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Predictive Power of Initial Oxygenation Index for Predicting Subsequent Bronchopulmonary Dysplasia in Premature Infants
Pediatric Research, 1999Co-Authors: Michael D. Schreiber, Babak Khoshnood, Pimol SrisuparpAbstract:Predictive Power of Initial Oxygenation Index for Predicting Subsequent Bronchopulmonary Dysplasia in Premature Infants
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Predictive Power of Oxygenation Index for Subsequent Development of Bronchopulmonary Dysplasia in Premature Infants
Pediatric Research, 1999Co-Authors: Pimol Srisuparp, Babak Khoshnood, Maria Corpuz, Sherwin E. Morgan, Michael D. SchreiberAbstract:Predictive Power of Oxygenation Index for Subsequent Development of Bronchopulmonary Dysplasia in Premature Infants
Pimol Srisuparp - One of the best experts on this subject based on the ideXlab platform.
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Predictive Power of Initial Oxygenation Index for Predicting Subsequent Bronchopulmonary Dysplasia in Premature Infants
Pediatric Research, 1999Co-Authors: Michael D. Schreiber, Babak Khoshnood, Pimol SrisuparpAbstract:Predictive Power of Initial Oxygenation Index for Predicting Subsequent Bronchopulmonary Dysplasia in Premature Infants
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Predictive Power of Oxygenation Index for Subsequent Development of Bronchopulmonary Dysplasia in Premature Infants
Pediatric Research, 1999Co-Authors: Pimol Srisuparp, Babak Khoshnood, Maria Corpuz, Sherwin E. Morgan, Michael D. SchreiberAbstract:Predictive Power of Oxygenation Index for Subsequent Development of Bronchopulmonary Dysplasia in Premature Infants
Haitao Yang - One of the best experts on this subject based on the ideXlab platform.
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Finite-Set Model Predictive Power Control of Brushless Doubly Fed Twin Stator Induction Generator
IEEE Transactions on Power Electronics, 2019Co-Authors: Ming Cheng, Haitao YangAbstract:This paper presents a finite-set model Predictive Power control (FS-MPPC) method for the brushless doubly fed twin stator induction generator (BDFTSIG) in variable speed constant frequency generation applications. The FS-MPPC controller is developed in a general reference frame from which all other reference frames can be deduced readily. The invariant feature of the Predictive Power model in various reference frames contributes to the reference frame-free characteristic of the developed FS-MPPC controller, enabling its application more flexible and universal. Besides, the arduous process of control winding flux estimation is avoided in the FS-MPPC controller by choosing state variables that are easy to be obtained. Moreover, the influence of rotor circuit that has long been neglected in the existing controllers for the brushless doubly fed induction machines is embedded within the Predictive Power model and inherently considered in the FS-MPPC controller, which contributes to accurate Power control of the BDFTSIG. Furthermore, the feasibility and effectiveness of the developed FS-MPPC controller regarding different Power levels and grid fault conditions are briefly discussed. Finally, numerical simulations and experimental tests are carried out, which demonstrates the effectiveness of the developed FS-MPPC controller.
