Open Access. Powered by Scholars. Published by Universities.®

Life Sciences Commons

Open Access. Powered by Scholars. Published by Universities.®

Articles 1 - 3 of 3

Full-Text Articles in Life Sciences

Feature Selection And Classification Of Maqc-Ii Breast Cancer And Multiple Myeloma Microarray Gene Expression Data, Qingzhong Liu, Andrew H. Sung, Zhongxue Chen, Jianzhong Liu, Xudong Huang, Youping Deng Dec 2009

Feature Selection And Classification Of Maqc-Ii Breast Cancer And Multiple Myeloma Microarray Gene Expression Data, Qingzhong Liu, Andrew H. Sung, Zhongxue Chen, Jianzhong Liu, Xudong Huang, Youping Deng

Faculty Publications

Microarray data has a high dimension of variables but available datasets usually have only a small number of samples, thereby making the study of such datasets interesting and challenging. In the task of analyzing microarray data for the purpose of, e.g., predicting gene-disease association, feature selection is very important because it provides a way to handle the high dimensionality by exploiting information redundancy induced by associations among genetic markers. Judicious feature selection in microarray data analysis can result in significant reduction of cost while maintaining or improving the classification or prediction accuracy of learning machines that are employed to ...


Subcellular Localization Of Marine Bacterial Alkaline Phosphatases, H. Luo, Ronald Benner, R. A. Long, Jianjun Hu Jan 2009

Subcellular Localization Of Marine Bacterial Alkaline Phosphatases, H. Luo, Ronald Benner, R. A. Long, Jianjun Hu

Faculty Publications

Bacterial alkaline phosphatases (APases) are important enzymes in organophosphate utilization in the ocean. The subcellular localization of APases has significant ecological implications for marine biota but is largely unknown. The extensive metagenomic sequence databases from the Global Ocean Sampling Expedition provide an opportunity to address this question. A bioinformatics pipeline was developed to identify marine bacterial APases from the metagenomic databases, and a consensus classification algorithm was designed to predict their subcellular localizations. We identified 3,733 bacterial APase sequences (including PhoA, PhoD, and PhoX) and found that cytoplasmic (41%) and extracellular (30%) APases exceed their periplasmic (17%), outer membrane ...


Integrative Disease Classification Based On Cross-Platform Microarray Data, C.-C. Liu, Jianjun Hu, M. Kalakrishnan, H. Huang, X. J. Zhou Jan 2009

Integrative Disease Classification Based On Cross-Platform Microarray Data, C.-C. Liu, Jianjun Hu, M. Kalakrishnan, H. Huang, X. J. Zhou

Faculty Publications

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 ...