New Generation Discovery: A Systematic View for Its Development, Issues and Future
KAUST DepartmentUniversity Library
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AbstractCollecting, storing, discovering, and locating are integral parts of the composition of the library. To fully utilize the library and achieve its ultimate value, the construction and production of discovery has always been a central part of the library’s practice and identity. That is the reason why the new generation (also called the next-generation discovery) discovery gets such striking effect since it came into library automation arena. However, when we talk about the new generation of discovery in the library domain, we should see it in the entirety of the library as one of its organic parts and consider its progress along with the evolution of the whole library world. We should have a deeper understanding about its relationship and interaction with the internet, the rapidly changing digital environment, and the elements and the chain of library services. To address above issues, this paper overviews the different versions of the definition for the new generation discovery by combining our own understanding. The paper also gives our own description for its properties and characteristics. The paper points out what challenges, which extends the technology domain to commercial interests and business strategy, are faced by the discovery applications, and how library and library professionals deal with those challenges. Finally, the paper elaborates on the promise brought by the new discovery development and what the next exploration might be for its future.
DescriptionThis paper is presented for the international conference: “Change and Challenge: Redefine the Future of Academic Libraries” on November 4-5th, 2012 in Beijing.
JournalSSRN Electronic Journal
Conference/Event nameChange and Challenge: Redefine the Future of Academic Libraries
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CLARM: An integrative approach for functional modules discoverySalem, Saeed M.; Alroobi, Rami; Banitaan, Shadi; Seridi, Loqmane; Brewer, James E.; Aljarah, Ibrahim (Association for Computing Machinery (ACM), 2011)Functional module discovery aims to find well-connected subnetworks which can serve as candidate protein complexes. Advances in High-throughput proteomic technologies have enabled the collection of large amount of interaction data as well as gene expression data. We propose, CLARM, a clustering algorithm that integrates gene expression profiles and protein protein interaction network for biological modules discovery. The main premise is that by enriching the interaction network by adding interactions between genes which are highly co-expressed over a wide range of biological and environmental conditions, we can improve the quality of the discovered modules. Protein protein interactions, known protein complexes, and gene expression profiles for diverse environmental conditions from the yeast Saccharomyces cerevisiae were used for evaluate the biological significance of the reported modules. Our experiments show that the CLARM approach is competitive to wellestablished module discovery methods. Copyright © 2011 ACM.
Predicting Causal Relationships from Biological Data: Applying Automated Casual Discovery on Mass Cytometry Data of Human Immune CellsTriantafillou, Sofia; Lagani, Vincenzo; Heinze-Deml, Christina; Schmidt, Angelika; Tegner, Jesper; Tsamardinos, Ioannis (Cold Spring Harbor Laboratory Press, 2017-03-31)Learning the causal relationships that define a molecular system allows us to predict how the system will respond to different interventions. Distinguishing causality from mere association typically requires randomized experiments. Methods for automated causal discovery from limited experiments exist, but have so far rarely been tested in systems biology applications. In this work, we apply state-of-the art causal discovery methods on a large collection of public mass cytometry data sets, measuring intra-cellular signaling proteins of the human immune system and their response to several perturbations. We show how different experimental conditions can be used to facilitate causal discovery, and apply two fundamental methods that produce context-specific causal predictions. Causal predictions were reproducible across independent data sets from two different studies, but often disagree with the KEGG pathway databases. Within this context, we discuss the caveats we need to overcome for automated causal discovery to become a part of the routine data analysis in systems biology.
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