Next Article in Journal
Synthesis of Diarylpyrazoles Containing a Phenylsulphone or Carbonitrile Moiety and their Chalcones as Possible Anti-Inflammatory Agents
Previous Article in Journal
A Single Gradient Stability-Indicating Reversed-Phase LC Method for the Estimation of Impurities in Omeprazole and Domperidone Capsules
Article Menu

Article Versions

Export Article

Scientia Pharmaceutica is published by MDPI from Volume 84 Issue 3 (2015). Articles in this Issue were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence. Articles are hosted by MDPI on as a courtesy and upon agreement with Austrian Pharmaceutical Society (Österreichische Pharmazeutische Gesellschaft, ÖPhG).
Open AccessArticle
Sci. Pharm. 2011, 79(3), 493-506; (registering DOI)

Feed Forward Artificial Neural Network: Tool for Early Detection of Ovarian Cancer

Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, 400076, MH, India
Department of Pharmaceutics, Adina Institute of Pharmaceutical Sciences, Sagar, 470002, M.P., India
Author to whom correspondence should be addressed.
Received: 11 May 2011 / Accepted: 5 July 2011 / Published: 5 July 2011
PDF [253 KB, uploaded 28 September 2016]


Pathological changes in an organ or tissue may be reflected in proteomic patterns in serum. The early detection of cancer is crucial for successful treatment. Some cancers affect the concentration of certain molecules in the blood, which allows early diagnosis by analyzing the blood mass spectrum. It is possible that exclusive serum proteomic patterns could be used to differentiate cancer samples from non-cancer ones. Several techniques have been developed for the analysis of mass-spectrum curve, and use them for the detection of prostate, ovarian, breast, bladder, pancreatic, kidney, liver, and colon cancers. In present study, we applied data mining to the diagnosis of ovarian cancer and identified the most informative points of the mass-spectrum curve, then used student t-test and neural networks to determine the differences between the curves of cancer patients and healthy people. Two serum SELDI MS data sets were used in this research to identify serum proteomic patterns that distinguish the serum of ovarian cancer cases from non-cancer controls. Statistical testing and genetic algorithm-based methods are used for feature selection respectively. The results showed that (1) data mining techniques can be successfully applied to ovarian cancer detection with a reasonably high performance; (2) the discriminatory features (proteomic patterns) can be very different from one selection method to another.
Keywords: Ovarian cancer; Neural networks; SELDI; Serum proteomics Ovarian cancer; Neural networks; SELDI; Serum proteomics
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

THAKUR, A.; MISHRA, V.; JAIN, S.K. Feed Forward Artificial Neural Network: Tool for Early Detection of Ovarian Cancer. Sci. Pharm. 2011, 79, 493-506.

Show more citation formats Show less citations formats

Article Metrics

Article Access Statistics



[Return to top]
Sci. Pharm. EISSN 2218-0532 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top