BioSunMS statistics

info info

Citations per year

Number of citations per year for the bioinformatics software tool BioSunMS
info

Tool usage distribution map

This map represents all the scientific publications referring to BioSunMS per scientific context
info info

Associated diseases

info

Popular tool citations

chevron_left Spectral measurement chevron_right
Want to access the full stats & trends on this tool?

BioSunMS specifications

Information


Unique identifier OMICS_26476
Name BioSunMS
Software type Application/Script
Interface Graphical user interface
Restrictions to use Academic or non-commercial use
Operating system Unix/Linux, Windows
Programming languages Java, R
License GNU General Public License version 2.0
Computer skills Medium
Version 1.0.1
Stability Beta
Requirements
SUN JVM, MySql
Maintained Yes

Versioning


No version available

Maintainers


  • person_outline Wuju Li
  • person_outline Rdfolder Team
  • person_outline Yuan Cao
  • person_outline Na Wang
  • person_outline Ailing Li

Publication for BioSunMS

BioSunMS citations

 (2)
library_books

Serum peptidomic profiling identifies a minimal residual disease detection and prognostic biomarker for patients with acute leukemia

2013
PMCID: 3813581
PMID: 24179540
DOI: 10.3892/ol.2013.1574

[…] d for their discriminatory power. The support vector machine (SVM) method is an effective algorithm for gene selection and cancer classification and was consequently used for class prediction (http://biosunms.sourceforge.net) (). The parameters in the Gaussian kernel function were optimized using the grid search approach (). The models of the training set were built using a selected number of peak […]

library_books

Two Classifiers Based on Serum Peptide Pattern for Prediction of HBV Induced Liver Cirrhosis Using MALDI TOF MS

2013
Biomed Res Int
PMCID: 3590609
PMID: 23509784
DOI: 10.1155/2013/814876

[…] ClinProt, including baseline subtraction of spectra, normalization and recalibration of a set of spectra, and internal peak alignment by using prominent peaks. Then, the processed data were stored in BioSunMS [] and were prepared for feature selection in WEKA. In BioSunMS, independent training set (n = 81) and the test sets (n = 81) were created. Based on the training set, peak statistics were don […]


Want to access the full list of citations?
BioSunMS institution(s)
Center of Computational Biology, Beijing Institute of Basic Medical Sciences, Beijing, China; National Center of Biomedical Analysis, Beijing, China
BioSunMS funding source(s)
Supported by Chinese Key Project for the Infectious Diseases 2008ZX10002-016.

BioSunMS reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review BioSunMS