DNA Methylation Prediction Using Reduced Features Obtained via Gappy Pair Kernel and Partial Least Square-Reference-Cited by-同舟云学术

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DNA Methylation Prediction Using Reduced Features Obtained via Gappy Pair Kernel and Partial Least Square

Published:2022 Issue: Volume:10 Page:53265-53274
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Shah Sajid¹^ORCID,Rahman Altaf Ur²,Jabeen Saima³^ORCID,Khan Ahmad²^ORCID,Khan Fiaz Gul²^ORCID,Elaffendi Mohammed¹^ORCID

Affiliation:

1. EIAS Laboratory, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

2. Department of Computer Science, COMSATS University Islamabad, Khyber Pakhtunkhwa, Pakistan

3. Department of IT and Computer Science, Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology, Mang, Haripur, Pakistan

Funder

EIAS Laboratory, CCIS, Prince Sultan University, Riyadh, Saudi Arabia

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/ielx7/6287639/9668973/09772620.pdf?arnumber=9772620

Reference88 articles.

1. The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses

2. KeBABS: an R package for kernel-based analysis of biological sequences: Fig. 1.

3. iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC

4. pLoc_bal-mGpos: Predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC

5. Identification of microRNA precursors using reduced and hybrid features

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