Identification of miRNA Biomarkers for Diverse Types of Cancers using
Statistical Learning Method at the Whole Genome Scale


Jnanendra Prasad Sarkar2,3,+, Indrajit Saha1,+,*, Adrian Lancucki4,+, Nimisha Ghosh5, Michl Wlasnowolski7, Grzegorz Bokota6,
Ashmita Dey2, Piotr Lipinski4 and Dariusz Plewczynski6,7,*


1Department of Computer Science and Engineering, National Institute of Technical Teachers' Training and Research, Kolkata, India
2Department of Computer Science and Engineering, Jadavpur University, Kolkata, India 3Larsen & Toubro Infotech Ltd., Pune, India.
4Computational Intelligence Research Group, Institute of Computer Science, University of Wroclaw, Poland
5Department of Computer Science and Information Technology, Institute of Technical Education and Research,
Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India
6Centre of New Technologies, University of Warsaw, Poland
7Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland
*Correspondence should be addressed to indrajit@nitttrkol.ac.in and d.plewczynski@cent.uw.edu.pl
+These authors contributed equally to this work



Predictor


Please submit Expression of 17 miRNAs in CSV format of size < 1MB as shown here




Supplementary


datasets


code


The SCES algorithm is implemented in MATLAB. The code is available in zipped form here. Use of algorithm is free as long as it is used for any academic and non-commercial purpose. If you use these algorithms, please cite the following reference:

J. P. Sarkar, I. Saha, A. Lancucki, N. Ghosh, M. Wlasnowolski, G. Bokota, A. Dey, P. Lipinski and D. Plewczynski, "Identification of miRNA Biomarkers for Diverse Types of Cancers using Statistical Learning Method at the Whole Genome Scale", submitted to Frontiers in Genetics (2019).

For any query regarding the algorithms, please mail to indrajit@nitttrkol.ac.in