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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

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Published on: May 17, 2019

A Novel Unsupervised Algorithm for Biological Process-based Analysis on Cancer.

Tianci Song¹, Sha Cao², Sheng Tao²

¹College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China.

Scientific Reports

|July 7, 2017

Summary

This summary is machine-generated.

This study introduces a new algorithm to quantify biological processes using gene expression data, improving cancer patient prognosis prediction. The method identifies key biological processes linked to clinical outcomes in breast cancer, aiding precision treatment.

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Area of Science:

Oncology
Bioinformatics
Computational Biology

Background:

Aberrant biological functions are hallmarks of cancer development.
High-throughput sequencing enables exploration of functional alterations in cancer progression.
Quantifying biological processes from gene expression data is crucial for understanding cancer.

Purpose of the Study:

To develop a novel algorithm for quantifying biological processes using gene expression profiles.
To identify biological processes associated with clinical outcomes in breast cancer.
To improve prognosis prediction and guide precision cancer treatment.

Main Methods:

Constructing principal curves to condense biological process information.
Implementing a novel scoring scheme for individualized quantification.
Applying the algorithm to large-scale breast cancer datasets for survival analysis.

Main Results:

Identified significant associations between specific biological processes and clinical outcomes in breast cancer.
Demonstrated the utility of the method in understanding cancer biology at the biological process level.
Achieved superior prognosis prediction accuracy compared to existing methods.

Conclusions:

The developed algorithm effectively quantifies biological processes from gene expression data.
This approach provides valuable insights into cancer biology and aids in precision medicine.
The method offers a promising tool for improving cancer patient prognosis and treatment strategies.