Protocols

Class assignment specifications

Information


Unique identifier OMICS_26979
Name Class assignment
Interface Web user interface
Restrictions to use None
Input data A matrix.
Input format TSV,(ZIP+CEL)
Output data A report file containing multiple evaluation statistics.
Computer skills Basic
Stability Stable
Maintained Yes

Maintainers


  • person_outline Enrico Glaab
  • person_outline Natalio Krasnogor
  • person_outline Jonathan Garibaldi

Publication for Class assignment

Class assignment citations

 (2)
library_books

Systematic assessment of cervical cancer initiation and progression uncovers genetic panels for deep learning based early diagnosis and proposes novel diagnostic and prognostic biomarkers

2017
Oncotarget
PMCID: 5752532
PMID: 29312619
DOI: 10.18632/oncotarget.22689

[…] of the fine-tuned models with the optimal hyperparameters were then measured on the test sets. The cut-off of the classification was the corresponding value that optimize F1-score at default.Ensemble class assignment analyses on the corresponding data sets of the whole transcriptome information were conducted using ArrayMining online software []. In brief, the data set was introduced to the Class […]

call_split

Using Rule Based Machine Learning for Candidate Disease Gene Prioritization and Sample Classification of Cancer Gene Expression Data

2012
PLoS One
PMCID: 3394775
PMID: 22808075
DOI: 10.1371/journal.pone.0039932
call_split See protocol

[…] on potential functional associations between genes; value range rules, which highlight the preferential up- or down-regulation of genes under different biological conditions and the robustness for a class assignment in terms of the relative width or narrowness of an expression value range; and default rules, which apply if none of the previous specific rules is matched. Each time a new decision r […]

Class assignment institution(s)
School of Computer Science, Nottingham University, Nottingham, UK
Class assignment funding source(s)
Supported by the Marie-Curie Early-Stage-Training programme (grant MEST-CT-2004-007597), by the UK Engineering and Physical Sciences Research Council (EP/E017215/1) and the Biotechnology and Biological Sciences Research Council (BB/F01855X/1).

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