Correlation-based feature selection of single cell transcriptomics data from multiple sources
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| Publié dans: | Journal of Big Data vol. 12, no. 1 (Jan 2025), p. 4 |
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| Publié: |
Springer Nature B.V.
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| Sujets: | |
| Accès en ligne: | Citation/Abstract Full Text - PDF |
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| 001 | 3152000877 | ||
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| 022 | |a 2196-1115 | ||
| 024 | 7 | |a 10.1186/s40537-024-01051-z |2 doi | |
| 035 | |a 3152000877 | ||
| 045 | 2 | |b d20250101 |b d20250131 | |
| 245 | 1 | |a Correlation-based feature selection of single cell transcriptomics data from multiple sources | |
| 260 | |b Springer Nature B.V. |c Jan 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a When applying data mining or machine learning techniques to large and diverse datasets, it is often necessary to construct descriptive and predictive models. Descriptive models are used to discover relationships between the attributes of the data while predictive models identify the characteristics of the data that will be collected in the future. Bioinformatics data is high-dimensional, making it practically impossible to apply the majority of “classical” algorithms for classification and clustering. Even if the algorithms are useful, training with large multidimensional data significantly increases processing time. The algorithms specialized for working with high-dimensional data often cannot process data containing large data sets with several thousand dimensions (features). Dimension reduction methods (such as PCA) do not provide satisfactory results, and also obscure the meaning of the original attributes in the data. For the constructed models to be usable, they must fulfill the requirement of scalability, as the amount of bioinformatics data is increasing rapidly. Furthermore, the significance of individual data features can differ from source to source. This paper describes an attribute selection method for efficient classification of high-dimensional (30,698) transcriptomics data collected from different sources. The proposed method was tested with 22 classification algorithms. The classification results for the selected attribute sets are comparable to the results for the complete attribute set. | |
| 653 | |a Multidimensional data | ||
| 653 | |a Algorithms | ||
| 653 | |a Data mining | ||
| 653 | |a Classification | ||
| 653 | |a Multidimensional methods | ||
| 653 | |a Machine learning | ||
| 653 | |a Bioinformatics | ||
| 653 | |a Prediction models | ||
| 653 | |a Clustering | ||
| 653 | |a Big Data | ||
| 653 | |a Data processing | ||
| 653 | |a Medical informatics | ||
| 653 | |a Data | ||
| 653 | |a Attributes | ||
| 653 | |a Information retrieval | ||
| 773 | 0 | |t Journal of Big Data |g vol. 12, no. 1 (Jan 2025), p. 4 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3152000877/abstract/embedded/160PP4OP4BJVV2EV?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3152000877/fulltextPDF/embedded/160PP4OP4BJVV2EV?source=fedsrch |