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Related Experiment Video

Updated: Jun 15, 2026

Surface-enhanced Resonance Raman Scattering Nanoprobe Ratiometry for Detecting Microscopic Ovarian Cancer via Folate Receptor Targeting
07:54

Surface-enhanced Resonance Raman Scattering Nanoprobe Ratiometry for Detecting Microscopic Ovarian Cancer via Folate Receptor Targeting

Published on: March 25, 2019

Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data.

Kai-Lin Tang1, Tong-Hua Li, Wen-Wei Xiong

  • 1Department of Chemistry, Tongji University, Shanghai, 200092, China.

BMC Bioinformatics
|March 2, 2010
PubMed
Summary

This study introduces a novel statistical moments method for reducing dimensions in proteomics data, improving early cancer detection. The approach demonstrates high accuracy in classifying ovarian cancer using SELDI-TOF mass spectrometry data.

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[TREATMENT OF FIRST METATARSAL DIAPHYSIS COMMINUTED FRACTURES WITH MINI-PLATE VIA MEDIAL APPROACH].

Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery·2015

Area of Science:

  • Proteomics
  • Biostatistics
  • Machine Learning

Background:

  • Surface-enhanced laser desorption/ionization-time of flight (SELDI-TOF) mass spectrometry shows potential for early cancer detection.
  • High dimensionality and classification pose challenges in analyzing SELDI-TOF data.
  • Ovarian cancer detection requires robust statistical methods for complex proteomic datasets.

Purpose of the Study:

  • To propose a novel dimensionality reduction method for high-throughput proteomics data.
  • To enhance classification performance in early cancer detection.
  • To validate the method using ovarian cancer SELDI-TOF data.

Main Methods:

  • A novel approach using statistical moments (mean, variance, skewness, kurtosis) for feature dimension reduction.

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  • Data intervals are defined and refined using t-testing.
  • Kernel Partial Least Squares (PLS) models are employed for improved classification efficiency.

    • Main Results:

    • Achieved high classification performance: average sensitivity 0.9950, specificity 0.9916, accuracy 0.9935.
    • Demonstrated strong correlation coefficient of 0.9869 over 100 five-fold cross-validations.
    • Showcased excellent performance with only one misclassified control in leave-one-out cross-validation.

    Conclusions:

    • The proposed statistical moments method is effective for analyzing high-throughput proteomics data.
    • This approach offers a viable solution for dimensionality reduction in cancer biomarker discovery.
    • The method shows significant promise for improving the accuracy of SELDI-TOF data analysis in clinical applications.