Afnizanfaizal Abdullah
Universiti Teknologi Malaysia

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Journal : Indonesian Journal of Electrical Engineering and Computer Science

Deep learning in non coding variant (a brief overview) Lee Kuan Xin; Afnizanfaizal Abdullah
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i3.pp1432-1438

Abstract

The 21st centuries were deemed to be the era of big data. Data driven research had become a necessity. This hold true not only in the business world, yet also in the field of biomedical world. From a few years of biological data extraction and derivation. With the advancement of Next Generation Sequencing, genomics data had grown to become an ambiguous giant which could not keep up with the pace of its advancement in it analysis counter parts. This results in a large amount of unanalysed genomic data. These genomic data consist not only plain information, researcher had discovered the potential of most gene called the non-coding variant and still failing in identifying their function. With the growth in volume of data, there is also a growth of hardware or technologies. With current technologies, we were able to implement a more complex and sophisticated algorithm in analysis these genomics data. The domain of deep learning had become a major interest of researcher as it was proven to have achieve a significant success in deriving insight from various field. This paper aims to review the current trend of non-coding variant analysis using deep learning approach.
Functional analysis of cancer gene subtype from co-clustering and classification Logenthiran Machap; Afnizanfaizal Abdullah
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 1: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i1.pp343-350

Abstract

Cancer is a heterogeneity genetic disease with huge phenotypic alterations among dissimilar cancers types or even between same cancer types. Recent expansions of genome-wide profiling technologies offer a chance to explore molecular changes variations throughout advancement of cancer. Therefore, various statistical and machine learning algorithms have been designed and developed for the handling and interpretation of high-throughput microarray molecular data. Discovery of molecular subtypes studies have permitted the cancer to be allocated into similar groups that are deliberated to port similar molecular and clinical characteristics. Thus, the main objective of this research is to discover cancer gene subtypes and classify genes to obtain higher accuracy. In particular improved co-clustering algorithm used to discover cancer subtypes. And then supervised infinite feature selection gene selection method was combined with multi class SVM for classification of selected genes and further biological analysis. The analysis on breast cancer and glioblastoma multiforme evidences that top genes involved in cancer and the pathways present in both cancer top genes. The functional analysis is useful in medical and pharmaceutical field for cancer diagnosis and prognosis.