Probabilistic Inference of Alternative Splicing Events in Microarray Data

Ofer Shai, Brendan J. Frey, Quaid D. Morris, Qun Pan, Christine Misquitta, Benjamin J. Blencowe

NeurIPS 2004 pp. 1241-1248

/neurips/2004/shai2004neurips-probabilistic/

Abstract

Alternative splicing (AS) is an important and frequent step in mammalian gene expression that allows a single gene to specify multiple products, and is crucial for the regulation of fundamental biological processes. The extent of AS regulation, and the mechanisms involved, are not well un- derstood. We have developed a custom DNA microarray platform for surveying AS levels on a large scale. We present here a generative model for the AS Array Platform (GenASAP) and demonstrate its utility for quantifying AS levels in different mouse tissues. Learning is performed using a variational expectation maximization algorithm, and the parame- ters are shown to correctly capture expected AS trends. A comparison of the results obtained with a well-established but low through-put experi- mental method demonstrate that AS levels obtained from GenASAP are highly predictive of AS levels in mammalian tissues.

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Cite

Text

Shai et al. "Probabilistic Inference of Alternative Splicing Events in Microarray Data." Neural Information Processing Systems, 2004.

Markdown

[Shai et al. "Probabilistic Inference of Alternative Splicing Events in Microarray Data." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/shai2004neurips-probabilistic/)

BibTeX

@inproceedings{shai2004neurips-probabilistic,
  title     = {{Probabilistic Inference of Alternative Splicing Events in Microarray Data}},
  author    = {Shai, Ofer and Frey, Brendan J. and Morris, Quaid D. and Pan, Qun and Misquitta, Christine and Blencowe, Benjamin J.},
  booktitle = {Neural Information Processing Systems},
  year      = {2004},
  pages     = {1241-1248},
  url       = {https://mlanthology.org/neurips/2004/shai2004neurips-probabilistic/}
}