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caCORRECT
For laboratories that produce many microarray chip data files, quality control is central to valid results. Some rely on quality control provided by microarray manufacturers and scanner hardware. Others use statistics software such as dChip, or routines provided in bioconductor (RMA, MAS, PLIER) to detect outliers in these experiments. caCORRECT represents the next-generation of microarray quality control technology that fuses spatial artifact detection (similar to Harshlighting) and model-based techniques to provide improved gene expression data quality in the presence of artifacts.    
Website: http://cacorrect.bme.gatech.edu/

  1. *Stokes TH, *Moffitt RA (equal contributing authors), Phan JH, and Wang MD. chip artifact CORRECTion (caCORRECT): A bioinformatics system for quality assurance of genomics and proteomics array data. Ann Biomed Eng. 2007 Jun; 35(6):1068-80.
  2. Moffitt RA, Yin-Goen Q, Stokes TH, Parry RM, Torrance JH, Phan JH, Young AN, and Wang MD. caCORRECT2: improving accuracy and reliability of microarray data in the presence of artifacts. BMC Bioinformatics. 2011 Sep 29; 12:383.
omniBiomarker
OmniBiomarker is a web-based bioinformatics application that addresses the microarray "curse of dimensionality" problem as well as the software standards problem. Biomarker identification from high-throughput microarray data for clinical prediction is sensitive to analysis parameters. As a result, candidate biomarker lists can be difficult to reproduce, limiting the efficiency of translating candidate biomarker lists to clinical applications. OmniBiomarker addresses this problem by tuning steps in the analysis pipeline to a clinical problem based on prior biological knowledge. By integrating knowledge in this manner, we can overcome the .curse-of-dimensionality. problem and increase the reproducibility of biomarker identification and clinical prediction. Furthermore, omniBiomarker will also include functionality for knowledge-driven data combination to increase the statistical power of biomarker identification. Finally, omniBiomarker addresses the problem of community accessibility. It is focused on not only the novelty of the analysis pipeline, but also on the integration of these analytical steps into a user-friendly, web-accessible interface. OmniBiomarker is now caBIG Silver level compliant, further increasing the interoperability of its functions with other bioinformatics tools in the cancer research community.    
Website: http://omnibiomarker.bme.gatech.edu/

  1. Phan JH, Yin-Goen Q, Young AN, and Wang MD. Improving the efficiency of biomarker identification using biological knowledge. Pac Symp Biocomput. 2009:427-38.
  2. Phan JH, Moffitt RA, Stokes TH, Liu J, Young AN, *Nie SM, and *Wang MD (co-corresponding senior authors). Convergence of biomarkers, bioinformatics and nanotechnology for individualized cancer treatment. Trends Biotechnol. 2009 Jun; 27(6):350-8.
  3. Phan JH, Young AN, and Wang MD. omniBiomarker: a web-based application for knowledge-driven biomarker identification. IEEE Trans Biomed Eng. In Press.
  4. Phan JH, Young AN, and Wang MD. Robust microarray meta-analysis identifies differentially expressed genes for clinical prediction. ScientificWorldJournal, Bioinformatics and Biomedical Informatics. 2012 Nov 28; 2012:989637.
ArrayWiki & TissueWiki
Wikis are valuable for data management because: 1) they have an intuitive interface for users to browse and contribute information, 2) they are searchable by Google and other web index/search engines and 3) they come with many tools to ensure data source attribution, conflict resolution, and data integrity maintenance. In the near future, if efforts such as the dbPedia project are successful, Wikis may be as useful as relational databases for executing queries on large repositories.

We seed our Wikis with data from public data repositories: ArrayWiki with Gene Expression Omnibus (GEO) and TissueWiki with Human Protein Atlas (HPA). We offer additional meta-data not offered by any other repository. We calculate data quality scores and combine data compression with data visualization in a novel data format based on open standards. The data quality scores allow users to better discriminate between analyses of low or high confidence.
   
Websites:
http://arraywiki.bme.gatech.edu/
http://tissuewiki.bme.gatech.edu/

  1. Stokes TH, Torrance JT, Li H, and Wang MD. ArrayWiki: an enabling technology for sharing public microarray data repositories and meta-analyses. BMC Bioinformatics. 2008 May 28; 9(Suppl 6):S18.
  2. Stokes TH, Moffitt RA, Hang S, and Wang MD. TissueWiki: a community repository of semantically annotated tissue images. BMC Bioinformatics. Under Review.
omniSpect
Multispectral imaging technologies capture spatial as well as spectral information from a sample. For example, quantum dots target specific biomarkers and emit different fluorescent spectra; or, mass spectrometry reveals different molecular distributions within a sample. OmniSpect untangles the contributions from different sources within a sample by leveraging their known spectral profiles or inferring them directly from the data.    
Website: http://omnispect.bme.gatech.edu/

  1. Parry RM, Galhena AS, Gamage CM, Bennett RV, Wang MD, and Fernandez FM. OmniSpect: An open MATLAB-based tool for visualization and analysis of matrix-assisted laser desorption/ionization and desorption electrospray ionization mass spectrometry images. J Am Soc Mass Spectrom. 2013 Feb 26; 1-4.
Q-IHC
Imaging modalities have been at the forefront of the fight against cancer by providing physicians with a variety of methods to make cancer diagnosis and prognosis. The increase in technology capabilities and data volume has lead to a critical need to help physicians make full use of the overwhelming information at hand. In addition, the traditional cancer diagnosis methods depend heavily on expert training, suffers from inter-observer variability, subjectivity and procedural inconsistencies. Quantitative cancer image analysis promises to address these issues by bringing consistency and accuracy to the results. Q-IHC is a set of cancer imaging analysis tools to assess Quantum Dots (QD) and Immunohistochemistry (IHC) based molecular and tissue images for various cancers types. This includes tools for semi-automatic segmentation, morphological feature analysis and quantification, color based region detection, automatic cell counting, and quantitative molecular profiling. With special emphasis on usability and usefulness, these tools will help accelerate research and contribute significantly to the fight against cancer.    
Download
Self-Extracting Installer: qIHC_pkg.exe
MATLAB Source Files: qIHC.zip
Instruction Manual: qIHC_User_Manual_ver2.pdf

  1. Xing Y, Chaudry Q, Shen C, Kong KY, Zhau HE, Chung LW, Petros JA, O.Regan RM, Yezhelyev MV, Simons JW, *Wang MD, and *Nie SM (co-corresponding senior authors). Bioconjugated quantum dots for multiplexed and quantitative immunohistochemistry. Nat Protoc. 2007 May 3; 2(5):1152-65.