The Experts below are selected from a list of 24 Experts worldwide ranked by ideXlab platform
Vonwun Soo - One of the best experts on this subject based on the ideXlab platform.
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identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and Conditional Independency
BMC Medical Genomics, 2009Co-Authors: Hsiangyuan Yeh, Shihwu Cheng, Yuchun Lin, Chengyu Yeh, Shihfang Lin, Vonwun SooAbstract:Prostate cancer is a world wide leading cancer and it is characterized by its aggressive metastasis. According to the clinical heterogeneity, prostate cancer displays different stages and grades related to the aggressive metastasis disease. Although numerous studies used microarray analysis and traditional clustering method to identify the individual genes during the disease processes, the important gene regulations remain unclear. We present a computational method for inferring genetic regulatory networks from micorarray data automatically with transcription factor analysis and Conditional independence testing to explore the potential significant gene regulatory networks that are correlated with cancer, tumor grade and stage in the prostate cancer. To deal with missing values in microarray data, we used a K-nearest-neighbors (KNN) algorithm to determine the precise expression values. We applied web services technology to wrap the bioinformatics toolkits and databases to automatically extract the promoter regions of DNA sequences and predicted the transcription factors that regulate the gene expressions. We adopt the microarray datasets consists of 62 primary tumors, 41 normal prostate tissues from Stanford Microarray Database (SMD) as a target dataset to evaluate our method. The predicted results showed that the possible biomarker genes related to cancer and denoted the androgen functions and processes may be in the development of the prostate cancer and promote the cell death in cell cycle. Our predicted results showed that sub-networks of genes SREBF1, STAT6 and PBX1 are strongly related to a high extent while ETS transcription factors ELK1, JUN and EGR2 are related to a low extent. Gene SLC22A3 may explain clinically the differentiation associated with the high grade cancer compared with low grade cancer. Enhancer of Zeste Homolg 2 (EZH2) regulated by RUNX1 and STAT3 is correlated to the pathological stage. We provide a computational framework to reconstruct the genetic regulatory network from the microarray data using biological knowledge and constraint-based inferences. Our method is helpful in verifying possible interaction relations in gene regulatory networks and filtering out incorrect relations inferred by imperfect methods. We predicted not only individual gene related to cancer but also discovered significant gene regulation networks. Our method is also validated in several enriched published papers and databases and the significant gene regulatory networks perform critical biological functions and processes including cell adhesion molecules, androgen and estrogen metabolism, smooth muscle contraction, and GO-annotated processes. Those significant gene regulations and the critical concept of tumor progression are useful to understand cancer biology and disease treatment.
Cory J Butz - One of the best experts on this subject based on the ideXlab platform.
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on the implication problem for probabilistic Conditional Independency
Systems Man and Cybernetics, 2000Co-Authors: S K M Wong, Cory J ButzAbstract:The implication problem is to test whether a given set of independencies logically implies another Independency. This problem is crucial in the design of a probabilistic reasoning system. We advocate that Bayesian networks are a generalization of standard relational databases. On the contrary, it has been suggested that Bayesian networks are different from the relational databases because the implication problem of these two systems does not coincide for some classes of probabilistic independencies. This remark, however, does not take into consideration one important issue, namely, the solvability of the implication problem. In this comprehensive study of the implication problem for probabilistic Conditional independencies, it is emphasized that Bayesian networks and relational databases coincide on solvable classes of independencies. The present study suggests that the implication problem for these two closely related systems differs only in unsolvable classes of independencies. This means there is no real difference between Bayesian networks and relational databases, in the sense that only solvable classes of independencies are useful in the design and implementation of these knowledge systems. More importantly, perhaps, these results suggest that many current attempts to generalize Bayesian networks can take full advantage of the generalizations made to standard relational databases.
S K M Wong - One of the best experts on this subject based on the ideXlab platform.
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on the implication problem for probabilistic Conditional Independency
Systems Man and Cybernetics, 2000Co-Authors: S K M Wong, Cory J ButzAbstract:The implication problem is to test whether a given set of independencies logically implies another Independency. This problem is crucial in the design of a probabilistic reasoning system. We advocate that Bayesian networks are a generalization of standard relational databases. On the contrary, it has been suggested that Bayesian networks are different from the relational databases because the implication problem of these two systems does not coincide for some classes of probabilistic independencies. This remark, however, does not take into consideration one important issue, namely, the solvability of the implication problem. In this comprehensive study of the implication problem for probabilistic Conditional independencies, it is emphasized that Bayesian networks and relational databases coincide on solvable classes of independencies. The present study suggests that the implication problem for these two closely related systems differs only in unsolvable classes of independencies. This means there is no real difference between Bayesian networks and relational databases, in the sense that only solvable classes of independencies are useful in the design and implementation of these knowledge systems. More importantly, perhaps, these results suggest that many current attempts to generalize Bayesian networks can take full advantage of the generalizations made to standard relational databases.
Hsiangyuan Yeh - One of the best experts on this subject based on the ideXlab platform.
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identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and Conditional Independency
BMC Medical Genomics, 2009Co-Authors: Hsiangyuan Yeh, Shihwu Cheng, Yuchun Lin, Chengyu Yeh, Shihfang Lin, Vonwun SooAbstract:Prostate cancer is a world wide leading cancer and it is characterized by its aggressive metastasis. According to the clinical heterogeneity, prostate cancer displays different stages and grades related to the aggressive metastasis disease. Although numerous studies used microarray analysis and traditional clustering method to identify the individual genes during the disease processes, the important gene regulations remain unclear. We present a computational method for inferring genetic regulatory networks from micorarray data automatically with transcription factor analysis and Conditional independence testing to explore the potential significant gene regulatory networks that are correlated with cancer, tumor grade and stage in the prostate cancer. To deal with missing values in microarray data, we used a K-nearest-neighbors (KNN) algorithm to determine the precise expression values. We applied web services technology to wrap the bioinformatics toolkits and databases to automatically extract the promoter regions of DNA sequences and predicted the transcription factors that regulate the gene expressions. We adopt the microarray datasets consists of 62 primary tumors, 41 normal prostate tissues from Stanford Microarray Database (SMD) as a target dataset to evaluate our method. The predicted results showed that the possible biomarker genes related to cancer and denoted the androgen functions and processes may be in the development of the prostate cancer and promote the cell death in cell cycle. Our predicted results showed that sub-networks of genes SREBF1, STAT6 and PBX1 are strongly related to a high extent while ETS transcription factors ELK1, JUN and EGR2 are related to a low extent. Gene SLC22A3 may explain clinically the differentiation associated with the high grade cancer compared with low grade cancer. Enhancer of Zeste Homolg 2 (EZH2) regulated by RUNX1 and STAT3 is correlated to the pathological stage. We provide a computational framework to reconstruct the genetic regulatory network from the microarray data using biological knowledge and constraint-based inferences. Our method is helpful in verifying possible interaction relations in gene regulatory networks and filtering out incorrect relations inferred by imperfect methods. We predicted not only individual gene related to cancer but also discovered significant gene regulation networks. Our method is also validated in several enriched published papers and databases and the significant gene regulatory networks perform critical biological functions and processes including cell adhesion molecules, androgen and estrogen metabolism, smooth muscle contraction, and GO-annotated processes. Those significant gene regulations and the critical concept of tumor progression are useful to understand cancer biology and disease treatment.
Masanori Fukushima - One of the best experts on this subject based on the ideXlab platform.
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evaluation for surrogacy of end points by using data from observational studies tumor downstaging for evaluating neoadjuvant chemotherapy in invasive bladder cancer
Clinical Cancer Research, 2006Co-Authors: Satoshi Teramukai, Hiroyuki Nishiyama, Yoshiyuki Matsui, Osamu Ogawa, Masanori FukushimaAbstract:Purpose: In clinical cancer trials for evaluating neoadjuvant chemotherapy, tumor downstaging is frequently used as a surrogate end point for overall survival. We evaluated the surrogacy of tumor downstaging using data from a follow-up observational study in bladder cancer. Experimental Design: A total of 586 patients (from 32 Japanese hospitals) who underwent radical cystectomy for invasive bladder cancer (clinical T2 to T4) between 1990 and 2000 were analyzed. We considered changes over time in clinical stage at diagnosis and pathologic stage at cystectomy as a surrogate end point, and survival time after cystectomy as a true end point. First, we developed a new criterion for tumor downstaging. Second, we statistically evaluated surrogacy for the criterion using Prentice9s criteria. Results: To develop the criterion of end points based on tumor downstaging, we selected the best classification among all possible classifications in an attempt to separate prognosis for patients. The hazard ratios after adjustment for prognostic factors in the intermediate effect patients and the poor effect patients were 1.9 (95% confidence interval, 1.0-3.7) and 5.0 (95% confidence interval, 2.6-9.8), respectively, compared with that in the good effect patients. The conditions for correlation and Conditional Independency of Prentice9s criteria were satisfied approximately. Neoadjuvant chemotherapy has a statistically significant tumor downstaging effect, whereas there was no difference on survival between treatment groups. Conclusions: The tumor downstaging effect could be an appropriate intermediate end point for screening novel neoadjuvant chemotherapy for invasive bladder cancer. The dataset from follow-up studies were useful for evaluating the surrogacy of end points.