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Updated: Jul 27, 2025

A Next-generation Tissue Microarray ngTMA Protocol for Biomarker Studies

A Next-generation Tissue Microarray ngTMA Protocol for Biomarker Studies

Published on: September 23, 2014

MTM: a multi-task learning framework to predict individualized tissue gene expression profiles.

Guangyi He¹, Maiyue Chen², Yingnan Bian³

¹Department of Medical Bioinformatics, School of Basic Medical Sciences, Peking University, Beijing 100191, China.

Bioinformatics (Oxford, England)

|June 6, 2023

Summary

This summary is machine-generated.

Predicting tissue gene expression from blood is possible using a new deep learning model. This multi-tissue transcriptome mapping (MTM) framework improves accuracy by leveraging cross-tissue information, aiding biomedical research.

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Last Updated: Jul 27, 2025

A Next-generation Tissue Microarray ngTMA Protocol for Biomarker Studies

A Next-generation Tissue Microarray ngTMA Protocol for Biomarker Studies

Published on: September 23, 2014

Predictive Immune Modeling of Solid Tumors

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Published on: February 25, 2020

TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis

TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis

Published on: June 8, 2020

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Transcriptome profiles are crucial for biological research but often require invasive biopsies.
Predicting tissue expression from accessible samples like blood is a promising alternative.
Current methods lack the ability to capture tissue-shared relevance, limiting predictive power.

Purpose of the Study:

To develop a novel deep learning framework for predicting individualized tissue expression profiles.
To overcome limitations of existing methods by integrating cross-tissue information.
To enable transcriptome analysis from non-invasively obtained samples.

Main Methods:

A unified deep learning-based multi-task learning framework named multi-tissue transcriptome mapping (MTM) was developed.
The framework jointly leverages individualized cross-tissue information from reference samples.
Multi-task learning was employed to enhance prediction accuracy.

Main Results:

MTM demonstrated superior sample-level and gene-level performance on unseen individuals.
The model accurately predicts individualized expression profiles from any tissue.
High prediction accuracy was achieved while preserving individualized biological variations.

Conclusions:

MTM offers a powerful tool for transcriptome analysis when invasive biopsies are not feasible.
The framework facilitates both fundamental and clinical biomedical research by enabling accessible transcriptome profiling.
MTM's ability to capture cross-tissue relevance enhances the utility of blood transcriptome data.