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Tissues

Tissues

Cells with similar structure and function are grouped into tissues. A group of tissues with a specialized function is called an organ. There are four main types of tissue in vertebrates: epithelial, connective, muscle, and nervous.

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Related Experiment Video

Updated: Feb 23, 2026

Reconstituting Cytoarchitecture and Function of Human Epithelial Tissues on an Open-Top Organ-Chip

Reconstituting Cytoarchitecture and Function of Human Epithelial Tissues on an Open-Top Organ-Chip

Published on: February 17, 2023

Predicting multicellular function through multi-layer tissue networks.

Marinka Zitnik¹, Jure Leskovec¹

¹Department of Computer Science, Stanford University, Stanford, CA, USA.

Bioinformatics (Oxford, England)

|September 9, 2017

Summary

This summary is machine-generated.

OhmNet, a new method, predicts protein functions across human tissues by analyzing multi-layer networks and tissue hierarchies. This approach improves accuracy and enables function transfer to uncharacterized tissues.

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Last Updated: Feb 23, 2026

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Area of Science:

Biomedical Informatics
Computational Biology
Network Science

Background:

Predicting tissue-specific protein functions is crucial for disease diagnostics and therapeutics.
Current methods face challenges in accurately modeling complex cellular functions across diverse human tissues.

Purpose of the Study:

To develop a novel computational approach, OhmNet, for unsupervised learning of protein features in multi-layer biological networks.
To leverage a multiscale tissue hierarchy to improve predictions of tissue-specific cellular functions.

Main Methods:

OhmNet employs a hierarchy-aware unsupervised node feature learning strategy on multi-layer networks, where each layer represents tissue-specific molecular interactions.
The algorithm learns low-dimensional neural embeddings for proteins, encouraging feature sharing based on network neighborhoods and tissue co-activation.
A multiscale tissue hierarchy is integrated to model inter-tissue relationships, enhancing generalization.

Main Results:

OhmNet demonstrated superior accuracy in predicting cellular functions across 48 human tissues compared to existing methods.
The approach generated more precise hypotheses regarding tissue-specific protein actions.
Leveraging the tissue hierarchy significantly improved predictive power and enabled functional transfer to uncharacterized tissues.

Conclusions:

OhmNet advances from flat network models to multiscale approaches for predicting cellular phenotypes.
The method offers a powerful tool for understanding tissue-specific protein functions and their roles in disease.
OhmNet facilitates the prediction and transfer of cellular functions, opening new avenues for therapeutic development.