Unraveling Genetic Complexity: Fine-tuning Machine Learning Models for DNA Sequence Analysis with K-mer Size-Reference-Cited by-同舟云学术

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Unraveling Genetic Complexity: Fine-tuning Machine Learning Models for DNA Sequence Analysis with K-mer Size

Published:2023-10-06 Issue: Volume: Page:
ISSN:
Container-title:2023 5th International Conference on Cybernetics and Intelligent System (ICORIS)
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Author:

Lukman ¹,Adnyana I Made Anggar Dwi²,Purnama Fajar³,Latumakulita Luther Alexander⁴,Rosalia Ayang Armelita¹,Pawana I Wayan Adi Juliawan²

Affiliation:

1. Indonesia University of Education,Dept. of Marine Information System,Bandung,Indonesia

2. Udayana University,Dept. of Electrical Engineering,Denpasar,Indonesia

3. Udayana University,Dept. of Information Technology,Denpasar,Indonesia

4. Sam Ratulangi University,Dept. of Information System,Manado,Indonesia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10352108/10352178/10352201.pdf?arnumber=10352201

Reference36 articles.

1. DNA sequencing | genetics;Griffiths,2012

2. Whole-genome sequencing approaches for conservation biology: Advantages, limitations and practical recommendations

3. Biological Sources of DNA: The Target Materials for Forensic DNA Typing

4. What Is Machine Learning?

5. Review on the Application of Machine Learning Algorithms in the Sequence Data Mining of DNA

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