A novel somatic cancer gene-based biomedical document feature ranking and clustering model
Open accessAbstract
Background As the size of somatic genomes in biomedical repositories increases, it is essential to predict cancer related document sets using the machine learning models. Most of the traditional gene-based somatic cancer mining models are independent of somatic gene ranking and feature extraction due to high computational cost and memory for large datasets. A wide range of feature selection and feature extraction strategies are existing, and they are by and large generally utilized in various areas. Every one of these strategies plans to expel repetitive and irrelevant features from the trained datasets with the goal that the arrangement of new document data will be increasingly accurate. Data extraction is the activity of providing relevant data according to an information need from a collection of large resources of data Results Ranking consists of sorting the information offers according to some criterion, so that the “best” results appear in the top priority in the provided list. The mapping of somatic genomes and its equivalent words like synonyms to biomedical document ranking is intricate on vast biomedical document data sets. In order to overcome these limitations, a novel feature ranking based fuzzy clustering framework is designed and implemented on large biomedical databases Conclusion Experimental results are simulated with different cluster sizes and gene features for somatic document clustering. Experimental results proved that the present model has high computational cluster quality rate with document ranking for somatic gene-based document indexing.
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Experimental results have different cluster sizes with different gene features for clustering of somatic documents.
“Experimental results have different cluster sizes with different gene features for clustering of somatic documents.”
From Influential Usage of Big Data and Artificial Intelligence in Healthcare (2021) · cites this paper as [69]
The size of somatic genomes in biomedical repositories is increasing.
“In biomedical repositories, the size of somatic genomes is getting increase.”
From Influential Usage of Big Data and Artificial Intelligence in Healthcare (2021) · cites this paper as [69]
The existing model has a high quality of computational clustering.
“And their generated results prove that the existing model has a high quality of computational clustering [69].”
From Influential Usage of Big Data and Artificial Intelligence in Healthcare (2021) · cites this paper as [69]
Mining models of conventional gene-based somatic cancer are unhampered by somatic gene ranking and feature extraction due to high computational cost and memory of large datasets.
“Mining models of conventional gene-based somatic cancer are unhampered by somatic gene ranking and feature extraction due to high computational cost and memory of large datasets.”
From Influential Usage of Big Data and Artificial Intelligence in Healthcare (2021) · cites this paper as [69]
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Influential Usage of Big Data and Artificial Intelligence in Healthcare
Yang, Y. C., Islam, S. U., Noor, A., Khan, S., Afsar, W., & Nazir, S. (2021) · Computational and Mathematical Methods in Medicine · doi:10.1155/2021/5812499 · Cited by 68 · see citing works
“And their generated results prove that the existing model has a high quality of computational clustering [69].”
BackgroundRetracted
- Authors
- Bikku, T., & Paturi, R.
- Affiliations
- Vignan's Foundation for Science, Technology & Research (IN)
- Corresponding author
- Bikku, Thulasi
- ORCID
- Bikku, Thulasi
- Venue
- Informatics in Medicine Unlocked
- Publisher
- Elsevier BV
- Published
- 2019
- Volume
- 16
- Pages
- 100188
- ISSN
- 2352-9148 (ISSN-L)
- OpenAlex
- W2947742655
- Cited by
- 15
- References
- 27
- Citations by year
- 2025: 6 · 2024: 1 · 2023: 3 · 2021: 3 · 2019: 2
- FWCI
- 0.41
- Type
- journal article
- Language
- en
- Update notices
- erratum (2020-01-01)
- Open access
- gold
- doi.org
- Licence
- Locations
- Informatics in Medicine Unlocked (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Keywords
- document clustering, fuzzy clustering, feature selection
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