Early Diagnosis of Non-small-cell lung Carcinoma from Gene Expression Using t-Distributed Stochastic Neighbor Embedding

Early Diagnosis of Non-small-cell lung Carcinoma from Gene Expression Using t-Distributed Stochastic Neighbor Embedding

Over the past two decades, lung cancer has been a dominant malignant form of cancer. Around 80% of major lung cancers are non-small cell lung carcinoma (NSCLC). NSCLC is the major reason for death from a malignant disease worldwide. As a result, there is urgent interest in the improvement of innovat...

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Main Authors: Zarzar, Mouayad, Razak, Eliza, Htike@Muhammad Yusof, Zaw Zaw, Yusof, Faridah
Format: Conference or Workshop Item
Language:English
Published: 2015
Subjects:
T Technology (General)
Online Access:http://irep.iium.edu.my/48056/
http://irep.iium.edu.my/48056/
http://irep.iium.edu.my/48056/1/ID_123.pdf
id iium-48056
recordtype eprints
spelling iium-480562018-05-23T02:36:31Z http://irep.iium.edu.my/48056/ Early Diagnosis of Non-small-cell lung Carcinoma from Gene Expression Using t-Distributed Stochastic Neighbor Embedding Zarzar, Mouayad Razak, Eliza Htike@Muhammad Yusof, Zaw Zaw Yusof, Faridah T Technology (General) Over the past two decades, lung cancer has been a dominant malignant form of cancer. Around 80% of major lung cancers are non-small cell lung carcinoma (NSCLC). NSCLC is the major reason for death from a malignant disease worldwide. As a result, there is urgent interest in the improvement of innovative diagnostic noninvasive technologies that may enhance early diagnosis of the disease. One of the most promising techniques for early detection of cancerous cells depends on machine learning based on molecular cancer classification using gene expression profiling data. Current technological breakthroughs in gene expression profiling, specifically with DNA and oligonucleotide microarrays, permit the concomitant analysis of the expression of thousands of genes and also enables surveillance of disease prediction and progression of patient survival at the molecular level. For this reason, we attempted to come up with a machine-learning-based strategy called composite hypercubes on iterated random projections (CHIRP) in order to settle the problem of detection of early NSCLC from DNA biochip gene expression data. Furthermore, we utilized an unsupervised dimensionality reduction approach, named t-distributed stochastic neighbor embedding (T-SNE), to reduce computational complexity and to increase the efficiency of the developed system. The average accuracy obtained by the proposed system in terms of detection and diagnosis of early non-small cell lung cancer was 97.21871%. The empirical results prove that the combination of dimensionality reduction models with machine-learning algorithms can be effectively used for early detection of specific NSCLC tumor type. 2015 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/48056/1/ID_123.pdf Zarzar, Mouayad and Razak, Eliza and Htike@Muhammad Yusof, Zaw Zaw and Yusof, Faridah (2015) Early Diagnosis of Non-small-cell lung Carcinoma from Gene Expression Using t-Distributed Stochastic Neighbor Embedding. In: International Conference on Advances Technology in Telecommunication, Broadcasting, and Satellite, 26-27 Sep 2015, Jakarta, Indonesia. (In Press) http://telsatech.org/
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
topic T Technology (General)
spellingShingle T Technology (General)
Zarzar, Mouayad
Razak, Eliza
Htike@Muhammad Yusof, Zaw Zaw
Yusof, Faridah
Early Diagnosis of Non-small-cell lung Carcinoma from Gene Expression Using t-Distributed Stochastic Neighbor Embedding
description Over the past two decades, lung cancer has been a dominant malignant form of cancer. Around 80% of major lung cancers are non-small cell lung carcinoma (NSCLC). NSCLC is the major reason for death from a malignant disease worldwide. As a result, there is urgent interest in the improvement of innovative diagnostic noninvasive technologies that may enhance early diagnosis of the disease. One of the most promising techniques for early detection of cancerous cells depends on machine learning based on molecular cancer classification using gene expression profiling data. Current technological breakthroughs in gene expression profiling, specifically with DNA and oligonucleotide microarrays, permit the concomitant analysis of the expression of thousands of genes and also enables surveillance of disease prediction and progression of patient survival at the molecular level. For this reason, we attempted to come up with a machine-learning-based strategy called composite hypercubes on iterated random projections (CHIRP) in order to settle the problem of detection of early NSCLC from DNA biochip gene expression data. Furthermore, we utilized an unsupervised dimensionality reduction approach, named t-distributed stochastic neighbor embedding (T-SNE), to reduce computational complexity and to increase the efficiency of the developed system. The average accuracy obtained by the proposed system in terms of detection and diagnosis of early non-small cell lung cancer was 97.21871%. The empirical results prove that the combination of dimensionality reduction models with machine-learning algorithms can be effectively used for early detection of specific NSCLC tumor type.
format Conference or Workshop Item
author Zarzar, Mouayad
Razak, Eliza
Htike@Muhammad Yusof, Zaw Zaw
Yusof, Faridah
author_facet Zarzar, Mouayad
Razak, Eliza
Htike@Muhammad Yusof, Zaw Zaw
Yusof, Faridah
author_sort Zarzar, Mouayad
title Early Diagnosis of Non-small-cell lung Carcinoma from Gene Expression Using t-Distributed Stochastic Neighbor Embedding
title_short Early Diagnosis of Non-small-cell lung Carcinoma from Gene Expression Using t-Distributed Stochastic Neighbor Embedding
title_full Early Diagnosis of Non-small-cell lung Carcinoma from Gene Expression Using t-Distributed Stochastic Neighbor Embedding
title_fullStr Early Diagnosis of Non-small-cell lung Carcinoma from Gene Expression Using t-Distributed Stochastic Neighbor Embedding
title_full_unstemmed Early Diagnosis of Non-small-cell lung Carcinoma from Gene Expression Using t-Distributed Stochastic Neighbor Embedding
title_sort early diagnosis of non-small-cell lung carcinoma from gene expression using t-distributed stochastic neighbor embedding
publishDate 2015
url http://irep.iium.edu.my/48056/
http://irep.iium.edu.my/48056/
http://irep.iium.edu.my/48056/1/ID_123.pdf
first_indexed 2023-09-18T21:08:17Z
last_indexed 2023-09-18T21:08:17Z
_version_ 1777411080369209344

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