Open AccessPrediction of Aptamer Protein Interaction Using Random Forest Algorithm
Author(s) -
N. Manju,
C. M. Samiha,
S. P. Pavan Kumar,
H. L. Gururaj,
Francesco Flammini
Publication year - 2022
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2022.3172278
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Aptamers are oligonucleotides that may attach to amino acids, polypeptide, tiny compounds, allergens and living cell membrane. Therapeutics, bio sensing and diagnostics are all sectors where the aptamers may be used. In this work, we present a model based on Random Forest Algorithms to predict the interaction of aptamer and target proteins by combining their most prominent characteristics. Amino Acid Composition and Psuedo Amino Acid Composition were utilized to express desired data by employing physicochemical and structural features of the amino acids. The dominant features were selected using feature importance classifiers such as random forest and eXtreme Gradient Boosting. Compared to these, principal component analysis techniques yielded a good feature set. As a result, 98% accuracy is obtained for 50 principal components. Many relevant characteristics in defining aptamer target protein interactions were discovered after analysing the best set of features. Our prediction approach is expected to become a valuable tool for discovering aptamer-target interactions, and the traits chosen and studied in this work might give helpful insight into the process of Aptamer Protein interactions.