@article {49726,
	title = {Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality.},
	journal = {Cell},
	volume = {158},
	year = {2014},
	month = {2014 Aug 28},
	pages = {1199-209},
	abstract = {<p>Synthetic lethality occurs when the inhibition of two genes is lethal while the inhibition of each single gene is not. It can be harnessed to selectively treat cancer by identifying inactive genes in a given cancer and targeting their synthetic lethal (SL) partners. We present a data-driven computational pipeline for the genome-wide identification of SL interactions in cancer by analyzing large volumes of cancer genomic data. First, we show that the approach successfully captures known SL partners of tumor suppressors and oncogenes. We then validate SL predictions obtained for the tumor suppressor VHL. Next, we construct a genome-wide network of SL interactions in cancer and demonstrate its value in predicting gene essentiality and clinical prognosis. Finally, we identify synthetic lethality arising from gene overactivation and use it to predict drug efficacy. These results form a computational basis for exploiting synthetic lethality to uncover cancer-specific susceptibilities.</p>},
	keywords = {Breast Neoplasms, Cell Line, Tumor, Computational Biology, Data Mining, Genes, Tumor Suppressor, HUMANS, Neoplasms, Oncogenes, RNA, Small Interfering, workflow},
	issn = {1097-4172},
	doi = {10.1016/j.cell.2014.07.027},
	author = {Jerby-Arnon, Livnat and Pfetzer, Nadja and Waldman, Yedael Y and McGarry, Lynn and James, Daniel and Shanks, Emma and Seashore-Ludlow, Brinton and Weinstock, Adam and Geiger, Tamar and Clemons, Paul A and Gottlieb, Eyal and Ruppin, Eytan}
}
@article {38421,
	title = {The partitioned LASSO-patternsearch algorithm with application to gene expression data},
	journal = {BMC bioinformaticsBMC Bioinformatics},
	volume = {13},
	year = {2012},
	note = {http://www.ncbi.nlm.nih.gov/pubmed/22587526?dopt=Abstract},
	type = {10.1186/1471-2105-13-98},
	abstract = {BACKGROUND: In systems biology, the task of reverse engineering gene pathways from data has been limited not just by the curse of dimensionality (the interaction space is huge) but also by systematic error in the data. The gene expression barcode reduces spurious association driven by batch effects and probe effects. The binary nature of the resulting expression calls lends itself perfectly to modern regularization approaches that thrive in high-dimensional settings. RESULTS: The Partitioned LASSO-Patternsearch algorithm is proposed to identify patterns of multiple dichotomous risk factors for outcomes of interest in genomic studies. A partitioning scheme is used to identify promising patterns by solving many LASSO-Patternsearch subproblems in parallel. All variables that survive this stage proceed to an aggregation stage where the most significant patterns are identified by solving a reduced LASSO-Patternsearch problem in just these variables. This approach was applied to genetic data sets with expression levels dichotomized by gene expression bar code. Most of the genes and second-order interactions thus selected and are known to be related to the outcomes. CONCLUSIONS: We demonstrate with simulations and data analyses that the proposed method not only selects variables and patterns more accurately, but also provides smaller models with better prediction accuracy, in comparison to several alternative methodologies.},
	keywords = {algorithms, Breast Neoplasms, Computer simulation, Female, Gene expression, Gene Expression Profiling, Genomics, HUMANS, Models, Genetic},
	author = {Shi, Weiliang and Wahba, Grace and Irizarry, Rafael A. and H{\'e}ctor Corrada Bravo and Wright, Stephen J.}
}
