@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 {38136,
	title = {Bio-STEER: A Semantic Web workflow tool for Grid computing in the life sciences},
	journal = {Future Generation Comp SystFuture Generation Comp Syst},
	volume = {23},
	year = {2007},
	type = {DOI 10.1016/j.future.2006.07.011},
	abstract = {Life science research is becoming evermore computationally intensive. Hence, from a computational resource perspective, Grid computing provides a logical approach to meeting many of the computational needs of life science research. However, there are several barriers to the widespread use of Grid computing in life sciences. In this paper, we attempt to address one particular barrier: the difficulty of using Grid computing by life scientists. Life science research often involves connecting multiple applications together to form a workflow. This process of constructing a workflow is complex. When combined with the difficulty of using Grid services, composing a meaningful workflow using Grid services can present a challenge to life scientists. Our proposed solution is a Semantic Web-enabled computing environment, called Bio-STEER. In BioSTEER, bioinformatics Grid services are mapped to Semantic Web services, described in OWL-S. We also defined an ontology in OWL to model bioinformatics applications. A graphical user interface helps to construct a scientific workflow by showing a list of services that are semantically sound: that is, the output of one service is semantically compatible with the input of the connecting service. Bio-STEER can help users take full advantaue of Grid services through a user-friendly graphical user interface (GUI), which allows them to easily construct the workflows they need. (c) 2006 Elsevier B.V. All rights reserved.},
	keywords = {client/server, distributed, ENVIRONMENTS, integrated, interface, management, semantics, services, systems, user, web-base, workflow},
	author = {Lee, S. and Wang, T. D. and Hashmi, N. and Michael P. Cummings}
}
