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Classifying transcription factor targets and discovering relevant biological features

Dustin T Holloway, Mark Kon and Charles DeLisi*

Biology Direct 2008, 3:22 doi:10.1186/1745-6150-3-22

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Are substitution rates and RNA editing correlated?

Argelia Cuenca, Gitte Petersen, Ole Seberg, Jerrold I Davis, Dennis W Stevenson BMC Evolutionary Biology 2010, 10:349 (11 November 2010)

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Research article   Open Access

Searching for molecular markers in head and neck squamous cell carcinomas (HNSCC) by statistical and bioinformatic analysis of larynx-derived SAGE libraries

Nelson JF Silveira, Leonardo Varuzza, Ariane Machado-Lima, Marcelo S Lauretto, Daniel G Pinheiro, Rodrigo V Rodrigues, Patrícia Severino, Francisco G Nobrega, Wilson A Silva, Carlos A de B Pereira, Eloiza H Tajara BMC Medical Genomics 2008, 1:56 (11 November 2008)

Abstract | Full text | PDF | PubMed | Cited on BioMed Central

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In silico regulatory analysis for exploring human disease progression

Dustin T Holloway, Mark Kon, Charles DeLisi Biology Direct 2008, 3:24 (18 June 2008)

Abstract | Full text | PDF | PubMed | 1 comment | Editor’s summary

Machine learning methods are employed to predict targets of transcription factors in the human genome and further provide possible insights on Wt1 and its role in Wilms Tumor.

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