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Genomic Markers for Age Prediction
Synthetic Biology & Genetic Engineering
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About
This collection offers an in-depth study of age prediction utilizing machine learning techniques applied to multi-omics markers. It leverages genomic data to build models that estimate an individual's biological age. The primary purpose is to analyse the correlations between gene expression levels and different life stages to identify crucial biomarkers associated with aging.
Columns
The dataset tracks the expression level of various genes, providing numeric values for analysis. The full dataset includes twenty-one distinct genes. Key columns documented in the sample file,
test_rows.csv, include:- 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)
Distribution
The data is typically stored in CSV format. The sample file,
test_rows.csv, contains 104 valid records and provides 10 out of the 13 initial columns. No future updates are anticipated for this dataset.Usage
This resource is ideal for building and training machine learning models for age estimation, particularly regression and decision tree methods. It can also be used for analyzing gene expression patterns to identify key biomarkers related to aging. Furthermore, researchers can utilize it for identifying potential pharmacological targets linked to age-related illnesses. It is suggested that users begin with multivariate analysis (PCA) and feature selection to simplify dimensionality reduction before employing more complex methodologies like neural networks.
Coverage
This multi-omics dataset collects biological information obtained from individuals. The scope is focused on providing the necessary marker expression levels to facilitate machine learning approaches for individual age prediction.
License
CC0 1.0 Universal (Public Domain Dedication)
Who Can Use It
- Data Scientists and AI Developers: For creating, testing, and optimising machine learning models for biological age prediction.
- Geneticists and Biologists: For investigating mechanisms of biological aging and genomic associations.
- Pharmaceutical Researchers: For screening potential drug targets by analysing gene expression changes associated with age-related conditions.
Dataset Name Suggestions
- Genomic Markers for Age Estimation
- Multi-Omics Age Prediction Dataset
- Gene Expression Study for Biological Age
- Biomarker Dataset for Predictive Age Modelling
Attributes
Original Data Source: Genomic Markers for Age Prediction
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