.png){kind=icon}
.png){kind=icon}
Return
Mutual Information Optimization for Mass Spectra Data Alignment
.png){kind=icon}
PRE
AI
DOI：10.1109/TCBB.2011.80.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Signal alignments play critical roles in many clinical setting. This is the case of mass spectrometry (MS) data, an important component of many types of proteomic analysis. A central problem occurs when one needs to integrate (MS) data produced by different sources, e. g., different equipment and/or laboratories. In these cases, some form of data integration or data fusion may be necessary in order to discard some source-specific aspects and improve the ability to perform a classification task such as inferring the disease classes of patients. The need for new high-performance data alignments methods is therefore particularly important in these contexts. In this paper, we propose an approach based both on an information theory perspective, generally used in a feature construction problem, and the application of a mathematical programming task (i.e., the weighted bipartite matching problem). We present the results of a competitive analysis of our method against other approaches. The analysis was conducted on data from plasma/ethylenediaminetetraacetic acid of control and Alzheimer patients collected from three different hospitals. The results point to a significant performance advantage of our method with respect to the competing ones tested.
Keywords:
Optimization
information theory
medicine
medical informatics
proteomics
data integration
graph algorithms
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
3.4
Papers:
3.3K
Citations:
6.4K
Organization
View more organizations