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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
Identification of aberrant pathway and network activity from high-throughput data.
M F Ochs1, R Karchin, H Ressom
1Departments of Oncology and Health Science Informatics, Johns Hopkins University, Baltimore, MD 19075, USA. mfo@jhu.edu
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|December 2, 2010
Summary
Understanding cellular pathways and networks is crucial for disease research. Analyzing high-throughput data helps identify coordinated biological activity changes driving phenotypes, improving disease understanding and therapeutic targeting.
Area of Science:
- Systems biology
- Molecular biology
- Genomics
Background:
- Cancer and metabolic diseases are driven by coordinated changes in cellular pathways and networks, not just single gene mutations.
- Gene-focused analyses often fail to capture the full spectrum of molecular changes underlying complex diseases.
- Understanding biological activity in cellular pathways is essential for disease etiology.
Framework:
- The workshop explored methods to infer pathway and network alterations from high-throughput data.
- Focus on analyzing coordinated changes in biological activity that influence cellular phenotype.
- Developing tools for pathway and network analysis is key.
Implementation:
- High-throughput data analysis techniques were discussed.
- Approaches to link pathway changes to observable phenotypes were examined.
- The need for robust computational tools was highlighted.
Implications:
- Improved understanding of complex diseases like cancer and metabolic disorders.
- Enhanced ability to identify critical molecular targets for therapeutic intervention.
- Advancement of precision medicine through pathway-centric approaches.
