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A Decision Tree Based Classifier to Analyze Human Ovarian Cancer cDNA Microarray Datasets.

Meng-Hsiun Tsai^1,2, Hsin-Chieh Wang³, Guan-Wei Lee⁴

¹Department of Management Information System, National Chung Hsing University, No.250, Kuo Kuang Rd., Taichung City, 402, Taiwan. mht@nchu.edu.tw.

Journal of Medical Systems

|November 5, 2015

Summary

This study identifies 9 novel oncogenes for early ovarian cancer detection, improving upon insufficient CA-125 tumor marker tests. This discovery aids timely diagnosis and enhances patient survival rates.

Keywords:

C&RT CHAID Decision tree Ingenuity Pathway Analysis Ovarian cancer

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Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer

Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer

Published on: January 12, 2020

Area of Science:

Oncology
Bioinformatics
Genetics

Background:

Ovarian cancer is the deadliest gynecological disease with high mortality due to late-stage diagnosis.
Current serum tumor marker CA-125 lacks sufficient specificity and sensitivity for early ovarian cancer detection.
There is an urgent need for precise methods to detect ovarian cancer biomarkers at early stages.

Purpose of the Study:

To identify novel target genes for early ovarian cancer detection using bioinformatics algorithms.
To develop a genetic pathway model for understanding ovarian cancer progression.
To discover new oncogenes associated with ovarian cancer.

Main Methods:

Analysis of 9600 ovarian cancer-related genes using feature selection and decision tree algorithms.
Screening and identification of candidate target genes.
Pathway analysis using Ingenuity Pathway Analysis (IPA) software to model gene interactions.

Main Results:

Identification of 9 oncogenes associated with ovarian cancer.
Discovery of several previously unknown genes linked to ovarian cancer.
Development of a genetic pathway model illustrating gene interactions in different pathological stages.

Conclusions:

The identified oncogenes offer potential for improved early-stage ovarian cancer diagnosis.
This research provides a foundation for developing more accurate diagnostic tools.
The findings can assist clinicians in early diagnosis and treatment, potentially improving patient survival rates.