The benchmark gene-set activity datasets
are available to download here for who interesting in using these benchmark datasets to evaluate their
novel algorithms for analyzing these gene-set activities.
The gene-set activity datasets available for downloading are listed below:
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References
1. Turashvili G, Bouchal J, Baumforth K, Wei W, Dziechciarkova M, Ehrmann J, Klein J, Fridman E, Skarda J, Srovnal J, Hajduch M, Murray P, Kolar Z, Novel markers for differentiation of lobular and ductal invasive breast carcinomas by laser microdissection and microarray analysis, BMC Cancer, 7:55, 2007.
2. Richardson AL, Wang ZC, De Nicolo A, Lu X, Brown M, Miron A, Liao X, Iglehart JD, Livingston DM, and Ganesan S, X chromosomal abnormalities in basal-like human breast cancer, Cancer Cell, 9:121-132, 2006.
3. Hong Y, Ho KS, Eu KW, Cheah PY, A susceptibility gene set for early onset colorectal cancer that integrates diverse signaling pathways: implication for tumorigenesis, Clinical Cancer Research, 13: 1107- 1114, 2007.
4. Sabates-Bellver J, Van der Flier LG, de Palo M, Cattaneo E, Maake C, Rehrauer H, Laczko E, Kurowski MA, Bujnicki JM, Menigatti M, Luz J, Ranalli TV, Gomes V, Pastorelli A, Faggiani R, Anti M, Jiricny J, Clevers H, and Marra G, Transcriptome profile of human colorectal adenomas, Molecular Cancer Research, 5:1263-1275, 2007.
5. Spira A, Beane JE, Shah V, Steiling K, Liu G, Schembri F, Gliman S, Dumas YM, Calner P, Sebastiani P, Sridhar S, Beamis J, Lamb C, Anderson T, Gerry N, Keane J, Lenburg ME, Brody JS, Airway epithelial gene expression in the diagnostic evaluation of smokers with suspect lung cancer, Nature Medicine, 13:361-366, 2007.
6. Landi MT, Dracheva T, Rotunno M, Figueroa JD, Liu H, Dasgupta A, Mann RE, Fukuoka J, Hames M, Bergen AW, Murphy SE, Yang P, Pesatori AC, Consonni D, Bertazzi PA, Wacholder S, Shih JH, Caporaso NE, Jen J, Gene expression signature of cigarette smoking and its role in lung adenocarcinoma development and survival, PLoS One, 3:e1651, 2008.
7. Tarca AL, Lauria M, Unger M, Bilal E, Boue S, Kumar Dey K, Koeppl H, Martin F, Meyer P, Nandy P, Norel R, Peitsch M, Rice JJ, Romero R, Stolovitzky G, Talikka M, Xiang Y, Zechner C, Improver DSC collarborators, Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge, Bioinformatics, 29:2892-9, 2013.
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Simple Gene Expression Analysis
GAT provide the simple tools to visualize the gene expression data, identify differential expression genes for the preliminary study step.
***Not available yet!
Gene-set Activity Transformation
Instead of analyzing gene expression levels, many work have been done in converting them to another form namely Pathway activity or Gene-set activity by integrating with pathway or gene-set data. The pathway/gene-set activity have been successfull applied for disease classification. Here, we provide several gene-set activity transformation methods including CORR-based, NCFS-i, AFS, etc.
Machine Learning
This tool is integrated with WEKA (the data mining tool) to maximize the learning effectiveness from the data. This includes the feature selection, classification, clustering and model validation with various number of methods.
Intepretation of Result
After the machine learning processes is done, the results from those step would be more valuable, if it can be annotated its relationship to the phenotype outcome. In this version, we only allow user to link and intepret their result using KEGG pathway database via KEGG mapper.
***Currently undergoing construction.
Data Repository
The benchmark gene-set activity datasets are available to download for who interesting in using these benchmark datasets to evaluate their novel algorithms for analyzing these gene-set activities.
The gene-set activity datasets available .
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