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您的检索式：作者名="Jennifer DY"
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| 1 | Identification of SNP-containing regulatory motifs in the myelodysplastic syndromes model using SNP arrays and gene expression arrays显示文摘Myelodysplastic syndromes have increased in frequency and incidence in the American population, but patient prognosis has not significantly improved over the last decade. Such improvements could be realized if biomarkers for accurate diagnosis and prognostic stratification were successfully identified. In this study, we propose a method that associates two state-of-the-art array technologies-single nucleotide polymorphism (SNP) array and gene expression array-with gene motifs considered transcription factor -binding sites (TFBS). We are particularly interested in SNP-containing motifs introduced by genetic variation and mutation as TFBS. The potential regulation of SNP-containing motifs affects only when certain mutations occur. These motifs can be identified from a group of co-expressed genes with copy number variation. Then, we used a sliding window to identify motif candidates near SNPs on gene sequences. The candidates were filtered by coarse thresholding and fine statistical testing. Using the regression-based LARS-EN algorithm and a level-wise sequence combination procedure, we identified 28 SNP-containing motifs as candidate TFBS. We confirmed 21 of the 28 motifs with ChIP-chip fragments in the TRANSFAC database. Another six motifs were validated by TRANSFAC via searching binding fragments on coregulated genes. The identified motifs and their location genes can be considered potential biomarkers for myelodysplastic syndromes. Thus, our proposed method, a novel strategy for associating two data categories, is capable of integrating information from different sources to identify reliable candidate regulatory SNP-containing motifs introduced by genetic variation and mutation. | Jing Fan Jennifer G. Dy Chung-Che Chang Xiaobo Zhou | 2013 | Chinese Journal of Cancer2013，32，4： | 2 |
| 2 | Feature Selection for Unsupervised Learning 显示文摘 | Jennifer G dy Carla E Brodley | 2004 | Journal of Machine Learning Research2004，5，4： | 1 |
| 3 | Unsupervised feature selection applied to content-based retrieval of lung images显示文摘 | Jennifer DY Brodley C Kak AC | 2003 | IEEE Trans on Pattem Analysis and Machine Intelligence2003，25，3： | 1 |
| 4 | Harnessing the Power of GPUs to Speed Up Feature Selection for Outlier Detection显示文摘Acquiring a set of features that emphasize the differences between normal data points and outliers can drastically facilitate the task of identifying outliers. In our work, we present a novel non-parametric evaluation criterion for filter-based feature selection which has an eye towards the final goal of outlier detection. The proposed method seeks the subset of features that represent the inherent characteristics of the normal dataset while forcing outliers to stand out, making them more easily distinguished by outlier detection algorithms. Experimental results on real datasets show the advantage of our feature selection algorithm compared with popular and state-of-the-art methods. We also show that the proposed algorithm is able to overcome the small sample space problem and perform well on highly imbalanced datasets. Furthermore, due to the highly parallelizable nature of the feature selection, we implement the algorithm on a graphics processing unit(GPU)to gain significant speedup over the serial version. The benefits of the GPU implementation are two-fold, as its performance scales very well in terms of the number of features, as well as the number of data points. | Fatemeh Azmandian Member, IEEE, Ayse Yilmazer Student Member, IEEE, Jennifer G. Dy Member, IEEE Javed A. Aslam IEEE, Jennifer G. Dy Member, ACM David R. Kaeli Fellow, IEEE, Member, ACM | 2014 | Journal of Computer Science & Technology2014，29，3： | 0 |
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