Integracja Wielomomiczna Przestrzenna

Data Analysis Workflow

1. Data Acquisition: Gathering raw data from various sources.

2. Preprocessing: Cleaning, transforming, and preparing the data for analysis.

3. Feature Engineering: Selecting and creating relevant features from the data.

4. Model Training: Training machine learning models using the prepared data.

5. Evaluation: Assessing the performance of the trained models.

Spatial Multi-Omics Analysis

The primary goal of this study is to acquire spatial information alongside proteomics data, providing a comprehensive view of tumor microenvironment dynamics. Specifically, we aim to integrate and annotate the diverse niches within the tumor mass.

This spatial multi-omics approach allows us to identify regions with distinct molecular profiles, potentially revealing previously unrecognized mechanisms driving tumor progression and response to therapy. Furthermore, it enables a deeper understanding of cellular interactions within these complex environments.

To validate our findings, we will conduct thorough pathological analysis of selected tumors, correlating the data obtained from spatial multi-omics with traditional histological features. This validation step is crucial for ensuring the robustness and clinical relevance of our results.

Frequently asked questions

Wypróbuj na żywo

Wszystko powyżej działa bezpośrednio w Twojej przeglądarce — otwórz Michaelis-Menten Kinetics i zmieniaj parametry podczas działania. Nic nie jest instalowane ani przesyłane na serwer, cały model działa w jednej karcie.