Learning regulatory programs by threshold SVD regression.
نویسندگان
چکیده
We formulate a statistical model for the regulation of global gene expression by multiple regulatory programs and propose a thresholding singular value decomposition (T-SVD) regression method for learning such a model from data. Extensive simulations demonstrate that this method offers improved computational speed and higher sensitivity and specificity over competing approaches. The method is used to analyze microRNA (miRNA) and long noncoding RNA (lncRNA) data from The Cancer Genome Atlas (TCGA) consortium. The analysis yields previously unidentified insights into the combinatorial regulation of gene expression by noncoding RNAs, as well as findings that are supported by evidence from the literature.
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عنوان ژورنال:
- Proceedings of the National Academy of Sciences of the United States of America
دوره 111 44 شماره
صفحات -
تاریخ انتشار 2014