New Generation Discovery: A Systematic View for Its Development, Issues and Future
KAUST DepartmentUniversity Library
Permanent link to this recordhttp://hdl.handle.net/10754/313408
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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.
SignalSpider: Probabilistic pattern discovery on multiple normalized ChIP-Seq signal profilesWong, Kachun; Li, Yue; Peng, Chengbin; Zhang, Zhaolei (Oxford University Press (OUP), 2014-09-05)Motivation: Chromatin immunoprecipitation (ChIP) followed by high-throughput sequencing (ChIP-Seq) measures the genome-wide occupancy of transcription factors in vivo. Different combinations of DNA-binding protein occupancies may result in a gene being expressed in different tissues or at different developmental stages. To fully understand the functions of genes, it is essential to develop probabilistic models on multiple ChIP-Seq profiles to decipher the combinatorial regulatory mechanisms by multiple transcription factors. Results: In this work, we describe a probabilistic model (SignalSpider) to decipher the combinatorial binding events of multiple transcription factors. Comparing with similar existing methods, we found SignalSpider performs better in clustering promoter and enhancer regions. Notably, SignalSpider can learn higher-order combinatorial patterns from multiple ChIP-Seq profiles. We have applied SignalSpider on the normalized ChIP-Seq profiles from the ENCODE consortium and learned model instances. We observed different higher-order enrichment and depletion patterns across sets of proteins. Those clustering patterns are supported by Gene Ontology (GO) enrichment, evolutionary conservation and chromatin interaction enrichment, offering biological insights for further focused studies. We also proposed a specific enrichment map visualization method to reveal the genome-wide transcription factor combinatorial patterns from the models built, which extend our existing fine-scale knowledge on gene regulation to a genome-wide level. Availability and implementation: The matrix-algebra-optimized executables and source codes are available at the authors' websites: http://www.cs.toronto.edu/∼wkc/SignalSpider. Contact: Supplementary information: Supplementary data are available at Bioinformatics online.