proteomics-clock

Predict organ-specific biological age from Olink proteomic data using elastic net aging clocks.

Updated May 10, 2026

Search Tags:#proteomics #aging #olink #biological age #organ aging

Ready to run

npx skills add https://github.com/MubasherMohammed/opencode-BioInfo --skill proteomics-clock

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: proteomics-clock
Source: https://github.com/MubasherMohammed/opencode-BioInfo/tree/main/python/skills/proteomics-clock
Command: npx skills add https://github.com/MubasherMohammed/opencode-BioInfo --skill proteomics-clock

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, matplotlib, seaborn, requests, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill predicts organ-specific biological age from Olink proteomic data, enabling researchers to understand the biological aging process at a molecular level.

Core Features & Use Cases

Organ Aging Prediction: Computes biological age for 23 different organs using the Goeminne et al. (2025) elastic net aging clocks.
Input Formats: Accepts Olink NPX protein expression tables in CSV, TSV, and compressed formats.
Output: Provides a structured report with age predictions, missing protein reports, and visualizations.
Use Case: Researchers can use this Skill to analyze proteomic data and assess the biological age of specific organs, which can be useful in understanding aging processes and disease progression.

Quick Start

Run the 'proteomics-clock' skill with the input file 'olink_data.csv' and specify the desired organs and generation (e.g., 'Heart,Brain,Kidney --generation gen1 --output output_dir').

Frequently Asked Questions about proteomics-clock

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I predict organ-specific biological age from Olink proteomic data?▼

The elastic net aging clocks from Goeminne et al. (2025) predict organ-specific biological age by applying machine learning to Olink proteomic data. This method uses NPX protein expression levels to estimate the biological age of 23 distinct organs for aging research.

What file formats are accepted for Olink NPX protein expression input?▼

The accepted input formats for Olink NPX protein expression tables are CSV, TSV, and compressed file formats. These files are parsed using pandas to extract the protein expression data required for computing the biological age predictions.

How do I calculate biological age for specific organs like the Heart and Brain?▼

You calculate biological age for specific organs by running the analysis tool with your Olink data file and specifying the desired organs and generation. For example, you can input 'Heart,Brain,Kidney --generation gen1' to generate age predictions for those specific tissues.

Do I need pandas and numpy installed to run proteomic aging clock predictions?▼

Yes, you need pandas, numpy, matplotlib, seaborn, and requests installed to run proteomic aging clock predictions. These dependencies are required for data processing, numerical computations, generating visualizations, and handling any required API requests during analysis.

What outputs are generated when estimating biological age from Olink data?▼

Estimating biological age from Olink data generates a structured report containing age predictions, a missing protein report, and visualizations. These outputs are created using matplotlib and seaborn to help researchers assess biological aging across different organs visually.

Can I use this biological age prediction method for clinical disease progression research?▼

Yes, you can use this biological age prediction method for clinical disease progression research. The organ-specific aging clocks are applicable to both research and clinical settings studying aging and disease, providing molecular-level insights into how specific organs age.