Classification of antimicrobial peptides with imbalanced datasets

Francy L. Camacho; Rodrigo Torres; Raúl Ramos Pollán

doi:10.1117/12.2207525

22 December 2015 Classification of antimicrobial peptides with imbalanced datasets

Francy L. Camacho, Rodrigo Torres, Raúl Ramos Pollán

Proceedings Volume 9681, 11th International Symposium on Medical Information Processing and Analysis; 96810T (2015) https://doi.org/10.1117/12.2207525
Event: 11th International Symposium on Medical Information Processing and Analysis (SIPAIM 2015), 2015, Cuenca, Ecuador

Abstract

In the last years, pattern recognition has been applied to several fields for solving multiple problems in science and technology as for example in protein prediction. This methodology can be useful for prediction of activity of biological molecules, e.g. for determination of antimicrobial activity of synthetic and natural peptides. In this work, we evaluate the performance of different physico-chemical properties of peptides (descriptors groups) in the presence of imbalanced data sets, when facing the task of detecting whether a peptide has antimicrobial activity. We evaluate undersampling and class weighting techniques to deal with the class imbalance with different classification methods and descriptor groups. Our classification model showed an estimated precision of 96% showing that descriptors used to codify the amino acid sequences contain enough information to correlate the peptides sequences with their antimicrobial activity by means of learning machines. Moreover, we show how certain descriptor groups (pseudoaminoacid composition type I) work better with imbalanced datasets while others (dipeptide composition) work better with balanced ones.

Citation Download Citation

Francy L. Camacho, Rodrigo Torres, and Raúl Ramos Pollán "Classification of antimicrobial peptides with imbalanced datasets ", Proc. SPIE 9681, 11th International Symposium on Medical Information Processing and Analysis, 96810T (22 December 2015); https://doi.org/10.1117/12.2207525

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available