Article Per Year (5 Year)
p-Index From 2021 - 2026
1.857
P-Index
This Author published in this journals
All Journal JOIV : International Journal on Informatics Visualization Indonesian Journal of Electrical Engineering and Computer Science
Rohayanti Hassan, Rohayanti
Unknown Affiliation
Computer Science & IT

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 11 Documents
Search

 1   2 
A Microarray Data Pre-processing Method for Cancer Classification Hui, Tay Xin; Kasim, Shahreen; Md Fudzee, Mohd Farhan; Abdullah, Zubaile; Hassan, Rohayanti; Erianda, Aldo
JOIV : International Journal on Informatics Visualization Vol 6, No 4 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.4.1523

Abstract

The development of microarray technology has led to significant improvements and research in various fields. With the help of machine learning techniques and statistical methods, it is now possible to organize, analyze, and interpret large amounts of biological data to uncover significant patterns of interest. The exploitation of microarray data is of great challenge for many researchers. Raw gene expression data are usually vulnerable to missing values, noisy data, incomplete data, and inconsistent data. Hence, processing data before being applied for cancer classification is important. In order to extract the biological significance of microarray gene expression data, data pre-processing is a necessary step to obtain valuable information for further analysis and address important hypotheses. This study presents a detailed description of pre-processing data method for cancer classification. The proposed method consists of three phases: data cleaning, transformation, and filtering. The combination of GenePattern software tool and Rstudio was utilized to implement the proposed data pre-processing method. The proposed method was applied to six gene expression datasets: lung cancer dataset, stomach cancer dataset, liver cancer dataset, kidney cancer dataset, thyroid cancer dataset, and breast cancer dataset to demonstrate the feasibility of the proposed method for cancer classification. A comparison has been made to illustrate the differences between the dataset before and after data pre-processing.
Co-Authors Abbas, Sara Tarek ElSayed Abdullah, Zubaile Ahmad Raddi, Muhammad Faris Faisal Ahmad, Johanna Alde Alanda, Alde Aldo Erianda, Aldo Asmuni, Hishammuddin Bichi, Abdulkadir Abubakar Edzuan Zainodin, Muhammad Eg Su, Goh Firosha, Ardian Halim, Shahliza Abd Hidra Amnur Hui, Tay Xin Kai Yuen, Simon Chong Khairuddin, Alif Ridzuan Khairudin, Alif Ridzuan Mohamad, Radziah Mohd Farhan MD Fudzee, Mohd Farhan Mohd Fuaad, Nur Atiqah Mohd Shafie, Muhammad Luqman Moi, Sim Hiew Nurul Aswa Omar, Nurul Aswa Ramlan, Rohaizan Sahid, Mohd Zanes Salamat, Mohamad Aizi Samsudin, Ruhaidah Saringat, Zainuri Shahreen Kasim, Shahreen Sujon, Khaled Mahmud Tole Sutikno Weng, Fong Cheng Yit, Tan Wen Yong, Pang Yee Zainodin, Muhammad Edzuan Zakaria, Noor Hidayah Zakaria, Noor Hidayah Binti Zakaria, Zalmiyah