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Integrative Missing Value Estimation For Microarray Data, Jianjun Hu, H. Li, M. S. Waterman, X. J. Zhou
Integrative Missing Value Estimation For Microarray Data, Jianjun Hu, H. Li, M. S. Waterman, X. J. Zhou
Faculty Publications
Background
Missing value estimation is an important preprocessing step in microarray analysis. Although several methods have been developed to solve this problem, their performance is unsatisfactory for datasets with high rates of missing data, high measurement noise, or limited numbers of samples. In fact, more than 80% of the time-series datasets in Stanford Microarray Database contain less than eight samples.
Results
We present the integrative Missing Value Estimation method (iMISS) by incorporating information from multiple reference microarray datasets to improve missing value estimation. For each gene with missing data, we derive a consistent neighbor-gene list by taking reference data sets …
Emd: An Ensemble Algorithm For Discovering Regulatory Motifs In Dna Sequences, Jianjun Hu, Y. D. Yang, D. Kihara
Emd: An Ensemble Algorithm For Discovering Regulatory Motifs In Dna Sequences, Jianjun Hu, Y. D. Yang, D. Kihara
Faculty Publications
Background
Understanding gene regulatory networks has become one of the central research problems in bioinformatics. More than thirty algorithms have been proposed to identify DNA regulatory sites during the past thirty years. However, the prediction accuracy of these algorithms is still quite low. Ensemble algorithms have emerged as an effective strategy in bioinformatics for improving the prediction accuracy by exploiting the synergetic prediction capability of multiple algorithms.
Results
We proposed a novel clustering-based ensemble algorithm named EMD for de novo motif discovery by combining multiple predictions from multiple runs of one or more base component algorithms. The ensemble approach is …