Technology for reduction feature space for classification immunosignature data

Tracking #: 555-1535


Владимир АндрющенкоORCID logo
Alexander KoshechkinORCID logo
Olga RomanovichORCID logo
Daniel StamateORCID logo
Alexander ZamyatinORCID logo

Responsible editor: 

Núria Queralt Rosinach

Submission Type: 

Position Paper


Random sequences of peptides in a microchip make it possible to generate specific immunosignatures that can diagnose various diseases. A large number of features does not allow for the quick and efficient analysis of such data. In this study, we propose technology to reduce feature space using various methods. The proposed technology makes it possible with minimal computational costs to ensure the accuracy and reliability of the classification of immunosignature data. The technology was tested on samples formed from a set of real data with the introduction of noise at various levels. The efficiency of the proposed technology on all test samples with various classifiers used for further data analysis is shown. .



  • Reviewed

Data repository URLs: 

Date of Submission: 

Tuesday, January 29, 2019

Date of Decision: 

Thursday, April 11, 2019

Nanopublication URLs:



Solicited Reviews:

1 Comment

Meta-Review by Editor

I decided to reject this manuscript as three out of 4 reviewers (with high reviewer confidence on the topic) had an overall bad impression and suggested to reject it, while one reviewer (with medium reviewer confidence) is undecided. They all agree that the manuscript is poorly written which makes its review difficult, with poor, unclear and insufficient description of fundamental parts of a paper: the research question, method, experimental design, evaluation and discussion.

Núria Queralt Rosinach (