WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394

Browse

Filters

2014 - 20203

Settings

COAR Access Rights: search.filters.coarAccessRights.metadata only accessPublisher: search.filters.publisher.Oxford Univ Press

Search Results

Now showing 1 - 3 of 3
  • Article
    Citation - WoS: 24
    Citation - Scopus: 27
    PANOGA: a Web Server for Identification of SNP-Targeted Pathways From Genome-Wide Association Study Data
    (Oxford Univ Press, 2014-01-11) Bakir-Gungor, Burcu; Egemen, Ece; Sezerman, Osman Ugur
    Genome-wide association studies (GWAS) have revolutionized the search for the variants underlying human complex diseases. However, in a typical GWAS, only a minority of the single-nucleotide polymorphisms (SNPs) with the strongest evidence of association is explained. One possible reason of complex diseases is the alterations in the activity of several biological pathways. Here we present a web server called Pathway and Network-Oriented GWAS Analysis to devise functionally important pathways through the identification of SNP-targeted genes within these pathways. The strength of our methodology stems from its multidimensional perspective, where we combine evidence from the following five resources: (i) genetic association information obtained through GWAS, (ii) SNP functional information, (iii) protein-protein interaction network, (iv) linkage disequilibrium and (v) biochemical pathways.
  • Article
    Citation - WoS: 4
    Citation - Scopus: 4
    Integrated Querying and Version Control of Context-Specific Biological Networks
    (Oxford Univ Press, 2020) Cowman, Tyler; Coskun, Mustafa; Grama, Ananth; Koyuturk, Mehmet
    Motivation: Biomolecular data stored in public databases is increasingly specialized to organisms, context/pathology and tissue type, potentially resulting in significant overhead for analyses. These networks are often specializations of generic interaction sets, presenting opportunities for reducing storage and computational cost. Therefore, it is desirable to develop effective compression and storage techniques, along with efficient algorithms and a flexible query interface capable of operating on compressed data structures. Current graph databases offer varying levels of support for network integration. However, these solutions do not provide efficient methods for the storage and querying of versioned networks. Results: We present VerTIoN, a framework consisting of novel data structures and associated query mechanisms for integrated querying of versioned context-specific biological networks. As a use case for our framework, we study network proximity queries in which the user can select and compose a combination of tissue-specific and generic networks. Using our compressed version tree data structure, in conjunction with state-of-the-art numerical techniques, we demonstrate real-time querying of large network databases. Conclusion: Our results show that it is possible to support flexible queries defined on heterogeneous networks composed at query time while drastically reducing response time for multiple simultaneous queries. The flexibility offered by VerTIoN in composing integrated network versions opens significant new avenues for the utilization of ever increasing volume of context-specific network data in a broad range of biomedical applications. Availability and Implementation: VerTIoN is implemented as a C++ library and is available at http://compbio.case.edu/omics/software/vertion and https://github.com/tjcowman/vertion Contact: tyler.cowman@case.edu
  • Article
    Citation - WoS: 8
    Citation - Scopus: 10
    Developing Structural Profile Matrices for Protein Secondary Structure and Solvent Accessibility Prediction
    (Oxford Univ Press, 2019-04-01) Aydin, Zafer; Azginoglu, Nuh; Bilgin, Halil Ibrahim; Celik, Mete
    Motivation: Predicting secondary structure and solvent accessibility of proteins are among the essential steps that preclude more elaborate 3D structure prediction tasks. Incorporating class label information contained in templates with known structures has the potential to improve the accuracy of prediction methods. Building a structural profile matrix is one such technique that provides a distribution for class labels at each amino acid position of the target. Results: In this paper, a new structural profiling technique is proposed that is based on deriving PFAM families and is combined with an existing approach. Cross-validation experiments on two benchmark datasets and at various similarity intervals demonstrate that the proposed profiling strategy performs significantly better than Homolpro, a state-of-the-art method for incorporating template information, as assessed by statistical hypothesis tests.