# Early Detection of Ovarian Cancer by Wavelet Analysis of Protein Mass Spectra

arXiv: https://arxiv.org/abs/2207.07028 · HTML: https://mlsanity.com/paper/2207.07028/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 39 references that are in the map.
- First posted on arXiv 14 Jul 2022.

## Builds on (most cited first, 6 of 6)

- [Partial Distance Correlation with Methods for Dissimilarities](https://mlsanity.com/paper/1310.2926/) (2013)
- [Fast Computing for Distance Covariance](https://mlsanity.com/paper/1410.1503/) (2014)
- [A fast algorithm for computing distance correlation](https://mlsanity.com/paper/1810.11332/) (2018)
- [Independent Component Analysis via Distance Covariance](https://mlsanity.com/paper/1306.4911/) (2013)
- [Robust Fitting of Mixture Models using Weighted Complete Estimating Equations](https://mlsanity.com/paper/2004.03751/) (2020)
- [Robust Wavelet-based Assessment of Scaling with Applications](https://mlsanity.com/paper/2201.09320/) (2022)

## Built on by (most cited first, 2 of 2)

- [A Method for Detecting Murmurous Heart Sounds based on Self-similar Properties](https://mlsanity.com/paper/2306.05283/) (2023)
- [A Noise Resilient Approach for Robust Hurst Exponent Estimation](https://mlsanity.com/paper/2510.04811/) (2025)

## Abstract (arXiv)

Accurate and efficient detection of ovarian cancer at early stages is critical to ensure proper treatments for patients. Among the first-line modalities investigated in studies of early diagnosis are features distilled from protein mass spectra. This method, however, considers only a specific subset of spectral responses and ignores the interplay among protein expression levels, which can also contain diagnostic information. We propose a new modality that automatically searches protein mass spectra for discriminatory features by considering the self-similar nature of the spectra. Self-similarity is assessed by taking a wavelet decomposition of protein mass spectra and estimating the rate of level-wise decay in the energies of the resulting wavelet coefficients. Level-wise energies are estimated in a robust manner using distance variance, and rates are estimated locally via a rolling window approach. This results in a collection of rates that can be used to characterize the interplay among proteins, which can be indicative of cancer presence. Discriminatory descriptors are then selected from these evolutionary rates and used as classifying features. The proposed wavelet-based features are used in conjunction with features proposed in the existing literature for early stage diagnosis of ovarian cancer using two datasets published by the American National Cancer Institute. Including the wavelet-based features from the new modality results in improvements in diagnostic performance for early-stage ovarian cancer detection. This demonstrates the ability of the proposed modality to characterize new ovarian cancer diagnostic information.