Prediction of Aptamer Protein Interaction Using Random Forest Algorithm-Reference-Cited by-同舟云学术

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Prediction of Aptamer Protein Interaction Using Random Forest Algorithm

Published:2022 Issue: Volume:10 Page:49677-49687
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Manju N.¹^ORCID,Samiha C. M.²,Kumar S. P. Pavan²,Gururaj H. L.²^ORCID,Flammini Francesco³^ORCID

Affiliation:

1. Department of Information Science and Engineering, JSS Science and Technology University, Mysore, India

2. Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysore, India

3. IDSIA USI-SUPSI, University of Applied Sciences and Arts of Southern Switzerland, Manno, Switzerland

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09766308.pdf?arnumber=9766308

Reference33 articles.

1. An efficient machine learning approach to nephrology through iris recognition

2. Classification of drugs based on mechanism of action using machine learning techniques

3. Selecting critical features for data classification based on machine learning methods

4. A comparison of feature ranking techniques for fault diagnosis of ball bearing

5. Constructive Prediction of Potential RNA Aptamers for a Protein Target

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1. Intuitionistic fuzzy rough set model based on k-means and its application to enhance prediction of aptamer–protein interacting pairs;Journal of Ambient Intelligence and Humanized Computing;2024-07-23

2. A Fuzzy Signature-Based Approach for Recommendation Systems;2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE);2024-06-30

3. Aptamer-based Membrane Protein Analysis and Molecular Diagnostics;Chemical Research in Chinese Universities;2024-03-12

4. Heart Disease Prediction using XGBoost and Random Forest Models;2024 5th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI);2024-01-18

5. Tuar—Transitory—Instabilità ( T ² i ): An ML-Based Framework to Predict Transient Instability in a 7-Power Plant Network;IEEE Access;2024

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