IIUM Repository

Premalignant pancreatic cancer diagnosis using proteomic pattern analysis

Htike@Muhammad Yusof, Zaw Zaw (2014) Premalignant pancreatic cancer diagnosis using proteomic pattern analysis. In: International Conference on Biological and Medical Sciences (ICBMS 2014), 27-28 September 2014, Bali, Indonesia. (In Press)

[img] PDF - Submitted Version
Restricted to Repository staff only

Download (229kB) | Request a copy
Official URL: http://www.icbms.org/

Abstract

Pancreatic cancer is one of the deadliest cancers due to the fact that it does not exhibit symptoms in the early stages. Furthermore, when pancreatic cancer gets diagnosed, it is usually too late. Consequently, early diagnosis is highly essential. The dawn of proteomics has brought with it a glimpse of hope of uncovering biomarkers that can be indicative of early pancreatic cancer. Proteome profiling techniques have become popular in the recent years to try to make sense of high-dimensional proteomic data and to find discrepancies between proteomes of healthy samples and cancerous samples. However, the high dimensionality of proteomics data coupled with small sample size poses a challenge. In this paper, we propose a framework using a hybrid logistic tree technique together with a feature selection technique to diagnose premalignant pancreatic cancer. We have validated our framework on a pancreatic cancer peptide mass spectrometry dataset. Satisfactory preliminary experimental results demonstrate the efficacy of our framework.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: 6919/38059
Uncontrolled Keywords: pancreatic cancer, proteomic analysis, pattern recognition
Subjects: T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering
Depositing User: Mr. Zaw Zaw Htike
Date Deposited: 08 Sep 2014 11:57
Last Modified: 19 Jun 2018 14:22
URI: http://irep.iium.edu.my/id/eprint/38059

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year