Comprehensive analysis of machine learning based predictors for identifying DNase I hypersensitive sites-Reference-Cited by-同舟云学术

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Comprehensive analysis of machine learning based predictors for identifying DNase I hypersensitive sites

Published:2021-11-09 Issue: Volume: Page:
ISSN:
Container-title:2021 International Conference on Innovative Computing (ICIC)
language:
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Author:

Rasheed Muhammad Rashid¹,Gill Mehwish²,Subhani Muhammad Asif²,Arif Muhammad³,Ahmed Saeed²,Kabir Muhammd²

Affiliation:

1. Tiangong University,School of Computer Science and Technology,Tianjin,P.R. China

2. University of Management and Technology,School of System and Technology,Department of Comptuer Science,Lahore,Pakistan

3. University of Mangment and Technology,School of System and Technolog,Department of Informatics,Lahore,Pakistan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9692906/9691502/09693087.pdf?arnumber=9693087

Reference48 articles.

1. A Weighted Spatially Constrained Finite Mixture Model for Image Segmentation

2. Fuzzy-Based Model to Evaluate City Centric Parameters for Smart City

3. Cardiac Disorder Classification by Electrocardiogram Sensing Using Deep Neural Network

4. Identifying DNase I hypersensitive sites using multi-features fusion and F-score features selection via Chou's 5-steps rule

5. Predicting DNase I hypersensitive sites via un-biased pseudo trinucleotide composition

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