Gene network models robust to spatial scaling and noisy input

Hardway, Heather

doi:10.1016/j.mbs.2012.03.004

Cited by 2 publications

(1 citation statement)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ACM-BCB'12, October 7-10, 2012, Orlando, FL, USA ACM 978-1-4503-1670-5/12/10. [6]. Given a data source with so much noise and errors, it is expected that the clustering results are not satisfactory.…”

Section: Introductionmentioning

confidence: 99%

A graph-based cluster ensemble method to detect protein functional modules from multiple information sources

Zhang

et al. 2012

Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine

View full text Add to dashboard Cite

Many works have been done to identify functional modules in Protein-Protein Interaction (PPI) networks but the results are far from satisfaction. One main reason is that the PPI data generated from high-throughput experiments is noisy and incomplete. Solving the problem goes beyond what a single data source can provide and thus requires the integration of multiple information sources. To address this problem, we hereby propose a graph-based cluster ensemble method which integrates gene ontology (GO) and gene expression data with PPI networks. Experimental results show that our method is superior to the baseline methods and demonstrate the benefits of integrating multiple biological information sources and diverse clustering methods.

show abstract

Section: Introductionmentioning

confidence: 99%

A graph-based cluster ensemble method to detect protein functional modules from multiple information sources

Zhang

et al. 2012

Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine

View full text Add to dashboard Cite

show abstract

Preparation of surgical meshes using self-regulating technology based on reaction-diffusion processes

Polyák,

Vadász,

Tátraaljai

et al. 2024

Med Biol Eng Comput

View full text Add to dashboard Cite

While reaction-diffusion processes are utilized in multiple scientific fields, these phenomena have seen limited practical application in the polymer industry. Although self-regulating processes driven by parallel reaction and diffusion can lead to patterned structures, most polymeric products with repeating subunits are still prepared by methods that require complex and expensive instrumentation. A notable, high-added-value example is surgical mesh, which is often manufactured by weaving or knitting. In our present work, we demonstrate how the polymer and the biomedical industry can benefit from the pattern-forming capabilities of reaction-diffusion. We would like to propose a self-regulating method that facilitates the creation of surgical meshes from biocompatible polymers. Since the control of the process assumes a thorough understanding of the underlying phenomena, the theoretical background, as well as a mathematical model that can accurately describe the empirical data, is also introduced and explained. Our method offers the benefits of conventional techniques while introducing additional advantages not attainable with them. Most importantly, the method proposed in this paper enables the rapid creation of meshes with an average pore size that can be adjusted easily and tailored to fit the intended area of application. Graphical abstract

show abstract

Gene network models robust to spatial scaling and noisy input

Cited by 2 publications

References 68 publications

A graph-based cluster ensemble method to detect protein functional modules from multiple information sources

A graph-based cluster ensemble method to detect protein functional modules from multiple information sources

Preparation of surgical meshes using self-regulating technology based on reaction-diffusion processes

Contact Info

Product

Resources

About