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Updated: Jan 4, 2026

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

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Published on: June 27, 2020

Semi-Supervised Topological Analysis for Elucidating Hidden Structures in High-Dimensional Transcriptome Datasets.

Tianshu Feng, Jaime I Davila, Yuanhang Liu

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|November 2, 2019

Summary

This summary is machine-generated.

This study introduces a semi-supervised topological analysis (STA) framework that integrates domain knowledge into topological data analysis (TDA). STA enhances data interpretation by selecting relevant filter functions for improved data relationship mining.

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Area of Science:

Computational Biology
Data Science
Topology

Background:

Topological Data Analysis (TDA) offers powerful dimensionality reduction and relationship mining.
Existing TDA frameworks lack integration of domain context and prior knowledge.
The Mapper algorithm is a key TDA tool for data projection and topology reconstruction.

Purpose of the Study:

To develop and evaluate a novel semi-supervised topological analysis (STA) framework.
To incorporate discrete or continuous labeled data points into TDA.
To enable selection of the most relevant filter functions based on domain context.

Main Methods:

Developed a semi-supervised topological analysis (STA) framework.
Incorporated labeled data points to guide filter function selection.
Validated the framework using simulation data and real-world biological datasets.

Main Results:

The STA framework successfully integrates domain context into TDA.
Graphs generated by STA for gene expression profiles align with existing biological knowledge.
Demonstrated effectiveness on Genotype-Tissue Expression and ovarian cancer transcriptome data.

Conclusions:

The proposed STA framework enhances TDA by incorporating prior knowledge.
STA provides a more biologically relevant representation of complex datasets.
This approach offers a valuable tool for analyzing high-dimensional biological data.