Many different aging clocks have emerged, including epigenomics, transcriptomics, and proteomics, which are capable of predicting biological age and providing information about the health of the organism. To help optimize the aging clock and identify targeted therapies for anti-aging, CD BioSciences provides correlation analysis services between proteomics and aging studies of targeted biological samples to discover proteins that change significantly with age and to construct proteomic aging clocks.
Proteomic Aging Clocks in Aging Studies
Age prediction and biological age estimation
We use proteomic aging clocks to predict an individual's biological age or estimate the rate of biological aging. The clock takes proteomic data from an individual as input and provides an age prediction or aging score as output.
Insights into aging mechanisms
By identifying the proteins that contribute most significantly to age prediction, our clients can gain valuable information about the biological processes and pathways that are associated with aging. This knowledge can contribute to a better understanding of the aging process and the identification of potential targets for interventions.
Our Development Services for Proteomic Aging Clocks
We help build the most accurate proteomic aging clocks, using state-of-the-art technology and powerful algorithms to view the biological age of your submitted samples.
- Protein data collection. We can collect protein data through two strategies, mainly including literature search and sample protein measurement, to comprehensively and efficiently build the database for prediction models.
- Statistical analysis. Age prediction uses statistical analytics to characterize proteomic changes in younger and older age, with specific protein changes selected as predictors.
- Aging clocks generation. We compile and train with the collected and prepared dataset to generate the predictor.
Proteomics Predictor Discovery
In our service, it is possible to search the data to identify a large number of common proteins that change with age, and we can also identify a small number of specific proteins that are proposed as potential predictors of age.
| Methods |
Descriptions |
| Literature search |
All proteins that change significantly with age are sorted out by searching proteomics studies on aging and age-related diseases in public resource databases. |
| Proteins |
We use high-throughput proteomics methods to obtain proteomic data from study organisms and identify proteins associated with actual age through linear regression processing of the data and sequencing support. |
Our Bioinformatic Statistical Analysis Services
We carry out bioinformatic statistical analysis of the aggregated protein data, with the following workflow.
- Data quality control. Transform the protein data to exclude outliers and control for data quality.
- Age-related protein identification. We adjust the parameters by running linear regression to find proteins that correlate with actual age.
- Unrelated protein deletion. We study which proteins are independent of each other and age by reverse linear regression and remove that factor from the model.
- Age-related disease association analysis. We can identify proteins among the identified proteins that can serve as biomarkers of age-related diseases by running regressions adjusted for age and related disease correlations.
At CD BioSciences, our proteomic aging clocks apply to a wide range of biological samples and have broad applications in aging research. If you are interested in our services, please feel free to contact us or make an online inquiry.
All of our services and products are intended for preclinical research use only and cannot be used to diagnose, treat or manage patients.
