TFInfer: a tool for probabilistic inference of transcription factor activities

H. M. Shahzad Asif, Matthew D. Rolfe, Jeff Green, Neil D. LawrenceMagnus RattrayGuido Sanguinetti
,  26:2635-2636, 2010.

Abstract

**Summary**: TFInfer is a novel open access, standalone tool for genome-wide inference of transcription factor activities from gene expression data. Based on an earlier MATLAB version, the software has now been extended in a number of ways. It has been significantly optimised in terms of performance, and it was given novel functionality, by allowing the user to model both time series and data from multiple independent conditions. With a full documentation and intuitive graphical user interface, together with an in-built data base of yeast and Escherichia coli transcription factors, the software does not require any mathematical or computational expertise to be used effectively.\ **Availability**: **Contact**: [gsanguin@staffmail.ed.ac.uk](gsanguin@staffmail.ed.ac.uk)

Cite this Paper


BibTeX
@InProceedings{pmlr-v-asif-tfinfer10, title = {TFInfer: a tool for probabilistic inference of transcription factor activities}, author = {H. M. Shahzad Asif and Matthew D. Rolfe and Jeff Green and Neil D. Lawrence and Magnus Rattray and Guido Sanguinetti}, pages = {2635--2636}, year = {}, editor = {}, volume = {26}, url = {http://inverseprobability.com/publications/asif-tfinfer10.html}, abstract = {**Summary**: TFInfer is a novel open access, standalone tool for genome-wide inference of transcription factor activities from gene expression data. Based on an earlier MATLAB version, the software has now been extended in a number of ways. It has been significantly optimised in terms of performance, and it was given novel functionality, by allowing the user to model both time series and data from multiple independent conditions. With a full documentation and intuitive graphical user interface, together with an in-built data base of yeast and Escherichia coli transcription factors, the software does not require any mathematical or computational expertise to be used effectively.\ **Availability**: **Contact**: [gsanguin@staffmail.ed.ac.uk](gsanguin@staffmail.ed.ac.uk)} }
Endnote
%0 Conference Paper %T TFInfer: a tool for probabilistic inference of transcription factor activities %A H. M. Shahzad Asif %A Matthew D. Rolfe %A Jeff Green %A Neil D. Lawrence %A Magnus Rattray %A Guido Sanguinetti %B %C Proceedings of Machine Learning Research %D %E %F pmlr-v-asif-tfinfer10 %I PMLR %J Proceedings of Machine Learning Research %P 2635--2636 %U http://inverseprobability.com %V %W PMLR %X **Summary**: TFInfer is a novel open access, standalone tool for genome-wide inference of transcription factor activities from gene expression data. Based on an earlier MATLAB version, the software has now been extended in a number of ways. It has been significantly optimised in terms of performance, and it was given novel functionality, by allowing the user to model both time series and data from multiple independent conditions. With a full documentation and intuitive graphical user interface, together with an in-built data base of yeast and Escherichia coli transcription factors, the software does not require any mathematical or computational expertise to be used effectively.\ **Availability**: **Contact**: [gsanguin@staffmail.ed.ac.uk](gsanguin@staffmail.ed.ac.uk)
RIS
TY - CPAPER TI - TFInfer: a tool for probabilistic inference of transcription factor activities AU - H. M. Shahzad Asif AU - Matthew D. Rolfe AU - Jeff Green AU - Neil D. Lawrence AU - Magnus Rattray AU - Guido Sanguinetti BT - PY - DA - ED - ID - pmlr-v-asif-tfinfer10 PB - PMLR SP - 2635 DP - PMLR EP - 2636 L1 - UR - http://inverseprobability.com/publications/asif-tfinfer10.html AB - **Summary**: TFInfer is a novel open access, standalone tool for genome-wide inference of transcription factor activities from gene expression data. Based on an earlier MATLAB version, the software has now been extended in a number of ways. It has been significantly optimised in terms of performance, and it was given novel functionality, by allowing the user to model both time series and data from multiple independent conditions. With a full documentation and intuitive graphical user interface, together with an in-built data base of yeast and Escherichia coli transcription factors, the software does not require any mathematical or computational expertise to be used effectively.\ **Availability**: **Contact**: [gsanguin@staffmail.ed.ac.uk](gsanguin@staffmail.ed.ac.uk) ER -
APA
Asif, H.M.S., Rolfe, M.D., Green, J., Lawrence, N.D., Rattray, M. & Sanguinetti, G.. (). TFInfer: a tool for probabilistic inference of transcription factor activities. , in PMLR :2635-2636

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