Literature DB >> 21325205

Integrating multiple types of data for signaling research: challenges and opportunities.

H Steven Wiley1.   

Abstract

New technologies promise to provide unprecedented amounts of information that can build a foundation for creating predictive models of cell signaling pathways. To be useful, however, this information must be integrated into a coherent framework. In addition, the sheer volume of data gathered from the new technologies requires computational approaches for its analysis. Unfortunately, there are many barriers to data integration and analysis, mostly because of a lack of adequate data standards and their inconsistent use by scientists. However, solving the fundamental issues of data sharing will enable the investigation of entirely new areas of cell signaling research.

Mesh:

Year:  2011        PMID: 21325205     DOI: 10.1126/scisignal.2001826

Source DB:  PubMed          Journal:  Sci Signal        ISSN: 1945-0877            Impact factor:   8.192


  7 in total

Review 1.  An engineering design approach to systems biology.

Authors:  Kevin A Janes; Preethi L Chandran; Roseanne M Ford; Matthew J Lazzara; Jason A Papin; Shayn M Peirce; Jeffrey J Saucerman; Douglas A Lauffenburger
Journal:  Integr Biol (Camb)       Date:  2017-07-17       Impact factor: 2.192

2.  III. Cellular ultrastructures in situ as key to understanding tumor energy metabolism: biological significance of the Warburg effect.

Authors:  Halina Witkiewicz; Phil Oh; Jan E Schnitzer
Journal:  F1000Res       Date:  2013-01-10

3.  Multi-omics-based label-free metabolic flux inference reveals obesity-associated dysregulatory mechanisms in liver glucose metabolism.

Authors:  Saori Uematsu; Satoshi Ohno; Kaori Y Tanaka; Atsushi Hatano; Toshiya Kokaji; Yuki Ito; Hiroyuki Kubota; Ken-Ichi Hironaka; Yutaka Suzuki; Masaki Matsumoto; Keiichi I Nakayama; Akiyoshi Hirayama; Tomoyoshi Soga; Shinya Kuroda
Journal:  iScience       Date:  2022-02-04

4.  In vivo transomic analyses of glucose-responsive metabolism in skeletal muscle reveal core differences between the healthy and obese states.

Authors:  Toshiya Kokaji; Miki Eto; Atsushi Hatano; Katsuyuki Yugi; Keigo Morita; Satoshi Ohno; Masashi Fujii; Ken-Ichi Hironaka; Yuki Ito; Riku Egami; Saori Uematsu; Akira Terakawa; Yifei Pan; Hideki Maehara; Dongzi Li; Yunfan Bai; Takaho Tsuchiya; Haruka Ozaki; Hiroshi Inoue; Hiroyuki Kubota; Yutaka Suzuki; Akiyoshi Hirayama; Tomoyoshi Soga; Shinya Kuroda
Journal:  Sci Rep       Date:  2022-08-12       Impact factor: 4.996

5.  Correspondence between fMRI and SNP data by group sparse canonical correlation analysis.

Authors:  Dongdong Lin; Vince D Calhoun; Yu-Ping Wang
Journal:  Med Image Anal       Date:  2013-10-31       Impact factor: 8.545

6.  Group sparse canonical correlation analysis for genomic data integration.

Authors:  Dongdong Lin; Jigang Zhang; Jingyao Li; Vince D Calhoun; Hong-Wen Deng; Yu-Ping Wang
Journal:  BMC Bioinformatics       Date:  2013-08-12       Impact factor: 3.169

Review 7.  Towards structural systems pharmacology to study complex diseases and personalized medicine.

Authors:  Lei Xie; Xiaoxia Ge; Hepan Tan; Li Xie; Yinliang Zhang; Thomas Hart; Xiaowei Yang; Philip E Bourne
Journal:  PLoS Comput Biol       Date:  2014-05-15       Impact factor: 4.475

  7 in total

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