The Experts below are selected from a list of 160947 Experts worldwide ranked by ideXlab platform
Xianghong Jasmine Zhou - One of the best experts on this subject based on the ideXlab platform.
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Integrative Disease Classification based on cross-platform microarray data
BMC Bioinformatics, 2009Co-Authors: Chun-chi Liu, Mrinal Kalakrishnan, Haiyan Huang, Xianghong Jasmine ZhouAbstract:Background Disease Classification has been an important application of microarray technology. However, most microarray-based classifiers can only handle data generated within the same study, since microarray data generated by different laboratories or with different platforms can not be compared directly due to systematic variations. This issue has severely limited the practical use of microarray-based Disease Classification.
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Integrative Disease Classification based on cross-platform microarray data.
BMC bioinformatics, 2009Co-Authors: Chun-chi Liu, Mrinal Kalakrishnan, Haiyan Huang, Xianghong Jasmine ZhouAbstract:Disease Classification has been an important application of microarray technology. However, most microarray-based classifiers can only handle data generated within the same study, since microarray data generated by different laboratories or with different platforms can not be compared directly due to systematic variations. This issue has severely limited the practical use of microarray-based Disease Classification. In this study, we tested the feasibility of Disease Classification by integrating the large amount of heterogeneous microarray datasets from the public microarray repositories. Cross-platform data compatibility is created by deriving expression log-rank ratios within datasets. One may then compare vectors of log-rank ratios across datasets. In addition, we systematically map textual annotations of datasets to concepts in Unified Medical Language System (UMLS), permitting quantitative analysis of the phenotype "distance" between datasets and automated construction of Disease classes. We design a new Classification approach named ManiSVM, which integrates Manifold data transformation with SVM learning to exploit the data properties. Using the leave one dataset out cross validation, ManiSVM achieved the overall accuracy of 70.7% (68.6% precision and 76.9% recall) with many Disease classes achieving the accuracy higher than 80%. Our results not only demonstrated the feasibility of the integrated Disease Classification approach, but also showed that the Classification accuracy increases with the number of homogenous training datasets. Thus, the power of the integrative approach will increase with the continuous accumulation of microarray data in public repositories. Our study shows that automated Disease diagnosis can be an important and promising application of the enormous amount of costly to generate, yet freely available, public microarray data.
Chun-chi Liu - One of the best experts on this subject based on the ideXlab platform.
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Integrative Disease Classification based on cross-platform microarray data
BMC Bioinformatics, 2009Co-Authors: Chun-chi Liu, Mrinal Kalakrishnan, Haiyan Huang, Xianghong Jasmine ZhouAbstract:Background Disease Classification has been an important application of microarray technology. However, most microarray-based classifiers can only handle data generated within the same study, since microarray data generated by different laboratories or with different platforms can not be compared directly due to systematic variations. This issue has severely limited the practical use of microarray-based Disease Classification.
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Integrative Disease Classification based on cross-platform microarray data.
BMC bioinformatics, 2009Co-Authors: Chun-chi Liu, Mrinal Kalakrishnan, Haiyan Huang, Xianghong Jasmine ZhouAbstract:Disease Classification has been an important application of microarray technology. However, most microarray-based classifiers can only handle data generated within the same study, since microarray data generated by different laboratories or with different platforms can not be compared directly due to systematic variations. This issue has severely limited the practical use of microarray-based Disease Classification. In this study, we tested the feasibility of Disease Classification by integrating the large amount of heterogeneous microarray datasets from the public microarray repositories. Cross-platform data compatibility is created by deriving expression log-rank ratios within datasets. One may then compare vectors of log-rank ratios across datasets. In addition, we systematically map textual annotations of datasets to concepts in Unified Medical Language System (UMLS), permitting quantitative analysis of the phenotype "distance" between datasets and automated construction of Disease classes. We design a new Classification approach named ManiSVM, which integrates Manifold data transformation with SVM learning to exploit the data properties. Using the leave one dataset out cross validation, ManiSVM achieved the overall accuracy of 70.7% (68.6% precision and 76.9% recall) with many Disease classes achieving the accuracy higher than 80%. Our results not only demonstrated the feasibility of the integrated Disease Classification approach, but also showed that the Classification accuracy increases with the number of homogenous training datasets. Thus, the power of the integrative approach will increase with the continuous accumulation of microarray data in public repositories. Our study shows that automated Disease diagnosis can be an important and promising application of the enormous amount of costly to generate, yet freely available, public microarray data.
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Integrative Disease Classification based on cross-platform microarray data
BMC Bioinformatics, 2009Co-Authors: Chun-chi Liu, Mrinal Kalakrishnan, Haiyan Huang, Xianghong ZhouAbstract:Abstract Background Disease Classification has been an important application of microarray technology. However, most microarray-based classifiers can only handle data generated within the same study, since microarray data generated by different laboratories or with different platforms can not be compared directly due to systematic variations. This issue has severely limited the practical use of microarray-based Disease Classification. Results In this study, we tested the feasibility of Disease Classification by integrating the large amount of heterogeneous microarray datasets from the public microarray repositories. Cross-platform data compatibility is created by deriving expression log-rank ratios within datasets. One may then compare vectors of log-rank ratios across datasets. In addition, we systematically map textual annotations of datasets to concepts in Unified Medical Language System (UMLS), permitting quantitative analysis of the phenotype "distance" between datasets and automated construction of Disease classes. We design a new Classification approach named ManiSVM, which integrates Manifold data transformation with SVM learning to exploit the data properties. Using the leave one dataset out cross validation, ManiSVM achieved the overall accuracy of 70.7% (68.6% precision and 76.9% recall) with many Disease classes achieving the accuracy higher than 80%. Conclusion Our results not only demonstrated the feasibility of the integrated Disease Classification approach, but also showed that the Classification accuracy increases with the number of homogenous training datasets. Thus, the power of the integrative approach will increase with the continuous accumulation of microarray data in public repositories. Our study shows that automated Disease diagnosis can be an important and promising application of the enormous amount of costly to generate, yet freely available, public microarray data.
Hai-wei Shen - One of the best experts on this subject based on the ideXlab platform.
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A novel logistic regression model combining semi-supervised learning and active learning for Disease Classification
Scientific reports, 2018Co-Authors: Hua Chai, Yong Liang, Sai Wang, Hai-wei ShenAbstract:Traditional supervised learning classifier needs a lot of labeled samples to achieve good performance, however in many biological datasets there is only a small size of labeled samples and the remaining samples are unlabeled. Labeling these unlabeled samples manually is difficult or expensive. Technologies such as active learning and semi-supervised learning have been proposed to utilize the unlabeled samples for improving the model performance. However in active learning the model suffers from being short-sighted or biased and some manual workload is still needed. The semi-supervised learning methods are easy to be affected by the noisy samples. In this paper we propose a novel logistic regression model based on complementarity of active learning and semi-supervised learning, for utilizing the unlabeled samples with least cost to improve the Disease Classification accuracy. In addition to that, an update pseudo-labeled samples mechanism is designed to reduce the false pseudo-labeled samples. The experiment results show that this new model can achieve better performances compared the widely used semi-supervised learning and active learning methods in Disease Classification and gene selection.
Adni - One of the best experts on this subject based on the ideXlab platform.
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ISBI - Disease Classification and prediction via semi-supervised dimensionality reduction
Proceedings. IEEE International Symposium on Biomedical Imaging, 2011Co-Authors: Kayhan Batmanghelich, Kilian M. Pohl, Ben Taskar, Christos Davatzikos, AdniAbstract:We present a new semi-supervised algorithmfor dimensionality reduction which exploits information of unlabeled data in order to improve the accuracy of image-based Disease Classification based on medical images. We perform dimensionality reduction by adopting the formalismof constrainedmatrix decomposition of [1] to semi-supervised learning. In addition, we add a new regularization term to the objective function to better captur the affinity between labeled and unlabeled data. We apply our method to a data set consisting of medical scans of subjects classified as Normal Control (CN) and Alzheimer (AD). The unlabeled data are scans of subjects diagnosedwith Mild Cognitive Impairment (MCI), which are at high risk to develop AD in the future. We measure the accuracy of our algorithm in classifying scans as AD and NC. In addition, we use the classifier to predict which subjects with MCI will converge to AD and compare those results to the diagnosis given at later follow ups. The experiments highlight that unlabeled data greatly improves the accuracy of our classifier.
Mrinal Kalakrishnan - One of the best experts on this subject based on the ideXlab platform.
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Integrative Disease Classification based on cross-platform microarray data
BMC Bioinformatics, 2009Co-Authors: Chun-chi Liu, Mrinal Kalakrishnan, Haiyan Huang, Xianghong Jasmine ZhouAbstract:Background Disease Classification has been an important application of microarray technology. However, most microarray-based classifiers can only handle data generated within the same study, since microarray data generated by different laboratories or with different platforms can not be compared directly due to systematic variations. This issue has severely limited the practical use of microarray-based Disease Classification.
-
Integrative Disease Classification based on cross-platform microarray data.
BMC bioinformatics, 2009Co-Authors: Chun-chi Liu, Mrinal Kalakrishnan, Haiyan Huang, Xianghong Jasmine ZhouAbstract:Disease Classification has been an important application of microarray technology. However, most microarray-based classifiers can only handle data generated within the same study, since microarray data generated by different laboratories or with different platforms can not be compared directly due to systematic variations. This issue has severely limited the practical use of microarray-based Disease Classification. In this study, we tested the feasibility of Disease Classification by integrating the large amount of heterogeneous microarray datasets from the public microarray repositories. Cross-platform data compatibility is created by deriving expression log-rank ratios within datasets. One may then compare vectors of log-rank ratios across datasets. In addition, we systematically map textual annotations of datasets to concepts in Unified Medical Language System (UMLS), permitting quantitative analysis of the phenotype "distance" between datasets and automated construction of Disease classes. We design a new Classification approach named ManiSVM, which integrates Manifold data transformation with SVM learning to exploit the data properties. Using the leave one dataset out cross validation, ManiSVM achieved the overall accuracy of 70.7% (68.6% precision and 76.9% recall) with many Disease classes achieving the accuracy higher than 80%. Our results not only demonstrated the feasibility of the integrated Disease Classification approach, but also showed that the Classification accuracy increases with the number of homogenous training datasets. Thus, the power of the integrative approach will increase with the continuous accumulation of microarray data in public repositories. Our study shows that automated Disease diagnosis can be an important and promising application of the enormous amount of costly to generate, yet freely available, public microarray data.
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Integrative Disease Classification based on cross-platform microarray data
BMC Bioinformatics, 2009Co-Authors: Chun-chi Liu, Mrinal Kalakrishnan, Haiyan Huang, Xianghong ZhouAbstract:Abstract Background Disease Classification has been an important application of microarray technology. However, most microarray-based classifiers can only handle data generated within the same study, since microarray data generated by different laboratories or with different platforms can not be compared directly due to systematic variations. This issue has severely limited the practical use of microarray-based Disease Classification. Results In this study, we tested the feasibility of Disease Classification by integrating the large amount of heterogeneous microarray datasets from the public microarray repositories. Cross-platform data compatibility is created by deriving expression log-rank ratios within datasets. One may then compare vectors of log-rank ratios across datasets. In addition, we systematically map textual annotations of datasets to concepts in Unified Medical Language System (UMLS), permitting quantitative analysis of the phenotype "distance" between datasets and automated construction of Disease classes. We design a new Classification approach named ManiSVM, which integrates Manifold data transformation with SVM learning to exploit the data properties. Using the leave one dataset out cross validation, ManiSVM achieved the overall accuracy of 70.7% (68.6% precision and 76.9% recall) with many Disease classes achieving the accuracy higher than 80%. Conclusion Our results not only demonstrated the feasibility of the integrated Disease Classification approach, but also showed that the Classification accuracy increases with the number of homogenous training datasets. Thus, the power of the integrative approach will increase with the continuous accumulation of microarray data in public repositories. Our study shows that automated Disease diagnosis can be an important and promising application of the enormous amount of costly to generate, yet freely available, public microarray data.