Age Prediction for Individuals Using Multi-Omic Datasets
A Machine Learning Approach
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About this dataset
This dataset provides a comprehensive study of age prediction using machine learning based on multi-omics markers. It contains data from twenty-one different genes (RPA2_3, ZYG11A_4, F5_2, HOXC4_1, NKIRAS2_2, MEIS1_1, SAMD10_2, GRM2_9, TRIM59_5, LDB2
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How to use the dataset
This dataset collects multi-omics information from individuals to predict their age. It includes markers from various subtypes of cells, such as RPA2_3, ZYG11A_4, F5_2, HOXC4_1, NKIRAS2_2, MEIS1_1, SAMD10_2, GRM 2_9 TRIM59-5 LDB2-3 ELOVL 2-6 DDO 1 KLF14-2.
To use this dataset effectively requires knowledge in both genetic and machine learning techniques. For the former category the user must understand data mining approaches used in gene expression resolution while for the latter they must familiarize themselves with techniques such as regression methods and decision tree methods.
To get started working with this data set it is advised that users familiarize themselves with basic concepts such as multi variate analysis (PCA) and feature selection algorithms that may render dimensionality reduction easier before attempting more sophisticated methodology e.g., neural networks or support vector machines - these later techniques can provide more accurate predictions when properly tuned but require a greater learning curve than simpler models due to their complexity . Additionally utilize hyperparameter optimization processes which allow users to test multiple models quickly and see which approach yields the best results (given user’s computing resources).
Last but not least once a good model has been identified save it for future use , either through serializing it or saving its weights –don't forget!
Research Ideas
- Analyzing the correlation between gene expression levels and age to identify key biomarkers associated with certain life stages.
- Building machine learning models that can predict a person's age from their multi-omics data.
- Identifying potential drug targets based on changes in gene expression associated with age-related diseases
Acknowledgements
If you use this dataset in your research, please credit the original authors.
Data Source
License
License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.
Columns
File: test_rows.csv
| Column name |
Description |
| RPA2_3 |
Expression level of the RPA2 gene in the third sample. (Numeric) |
| ZYG11A_4 |
Expression level of the ZYG11A gene in the fourth sample. (Numeric) |
| F5_2 |
Expression level of the F5 gene in the second sample. (Numeric) |
| HOXC4_1 |
Expression level of the HOXC4 gene in the first sample. (Numeric) |
| NKIRAS2_2 |
Expression level of the NKIRAS2 gene in the second sample. (Numeric) |
| MEIS1_1 |
Expression level of the MEIS1 gene in the first sample. (Numeric) |
| SAMD10_2 |
Expression level of the SAMD10 gene in the second sample. (Numeric) |
| GRM2_9 |
Expression level of the GRM2 gene in the ninth sample. (Numeric) |
| TRIM59_5 |
Expression level of the TRIM59 gene in the fifth sample. (Numeric) |
| LDB2_3 |
Expression level of the LDB2 gene in the third sample. (Numeric) |
| ELOVL2_6 |
Expression level of the ELOVL2 gene in the sixth sample. (Numeric) |
| DDO_1 |
Expression level of the DDO gene in the first sample. (Numeric) |
| KLF14_2 |
Expression level of the KLF14 gene in the second sample. (Numeric) |
Acknowledgements
If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .