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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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Volume 9 | Issue 5
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| Paper Title: | Classification of DNA using Machine Learning |
| Authors Name: | CH.VISHAL , SHIVA KRISHNA , J.AKASH REDDY , NEELA LIKITH , N.CHIRANJEEVI |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_217809
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| Published Paper Id: | IJNRD2404238 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| Abstract: | DNA sequencing technologies have advanced rapidly, resulting in a massive increase in available genomic data. Machine learning techniques offer promising solutions for analyzing and extracting useful information from this big genomic data. This paper provides an overview of how machine learning can be applied to classify DNA sequences. The introduction gives background on DNA sequencing and machine learning, and explains the motivation for using machine learning for DNA classification. The methodology section describes common machine learning techniques used for DNA classification such as support vector machines, random forests, neural networks, and deep learning. It explains how these models are trained on labeled DNA data to classify new unknown samples. The literature review summarizes key research applying machine learning for tasks such as gene prediction, splice site detection, promoter recognition, and histone modification prediction. It highlights studies using both supervised and unsupervised learning approaches on datasets from various species. The review finds that machine learning models are able to effectively learn sequence patterns and make accurate predictions, outperforming traditional techniques in many cases. Deep learning methods in particular have achieved state-of-the-art performance due to their ability to automatically extract informative features. The literature identifies current challenges and limitations such as model interpretability as well as future directions for the field. |
| Keywords: | Classification of DNA using Machine Learning |
| Cite Article: | "Classification of DNA using Machine Learning", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.c66-c70, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404238.pdf |
| Downloads: | 00041 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2404238 Registration ID: 217809 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: c66-c70 Country: -, -, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404238 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404238 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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