Developing an automated machine learning approach to test discontinuity in DNA for detecting tuberculosis

Azizur Rahman; S. F.  Nimmy; G.  Sarowar

doi:10.1007/978-3-319-93351-1_23

Developing an automated machine learning approach to test discontinuity in DNA for detecting tuberculosis

Azizur Rahman, S. F. Nimmy, G. Sarowar

Research output: Book chapter/Published conference paper › Conference paper › peer-review

8 Citations (Scopus)

Abstract

Discontinuity in long DNA sequences creates harmful diseases like Tuberculosis (TB). Given the 21th centurys exponential growth of big-data environments, knowing the precise breaks position of DNA sequences is essential for many reasons including advanced medical intervention. This study designs an automated framework to assess the breaks positions in long DNA sequences which are responsible for TB and then empirically tests it by analyzing a big DNA dataset from the National Center for Biotechnology Information (NCBI) database. The method consists of a range of data cleansing and deep neural network tools for big data situation. Findings reveal that the proposed approach is better than other methods in detecting DNA sequence breaks for TB via resolving a sample size issue of the training dataset and recursively divide the whole dataset into certain length to detect the breaks. It also provides a faster predictive analysis with more accurate and reliable outcomes.

Original language	English
Title of host publication	Proceedings of the Twelfth International Conference on Management Science and Engineering Management
Editors	J. Paulo Davim
Place of Publication	Cham, Switzerland
Publisher	Springer
Chapter	23
Pages	277-286
Number of pages	10
ISBN (Electronic)	9783319933511
ISBN (Print)	9783319933504
DOIs	https://doi.org/10.1007/978-3-319-93351-1_23
Publication status	Published - 2019
Event	12th International Conference on Management Science and Engineering Management: ICMSEM 2018 - http://www.icmsem.org/index.html#past, Melbourne, Australia Duration: 01 Aug 2018 → 04 Aug 2018 http://www.icmsem.org/index.html#past

Conference

Conference	12th International Conference on Management Science and Engineering Management
Country/Territory	Australia
City	Melbourne
Period	01/08/18 → 04/08/18
Internet address	http://www.icmsem.org/index.html#past

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-3-319-93351-1_23

Cite this

@inproceedings{d07a0a50d5c749de894aa6a9632ce5c1,

title = "Developing an automated machine learning approach to test discontinuity in DNA for detecting tuberculosis",

abstract = "Discontinuity in long DNA sequences creates harmful diseases like Tuberculosis (TB). Given the 21th centurys exponential growth of big-data environments, knowing the precise breaks position of DNA sequences is essential for many reasons including advanced medical intervention. This study designs an automated framework to assess the breaks positions in long DNA sequences which are responsible for TB and then empirically tests it by analyzing a big DNA dataset from the National Center for Biotechnology Information (NCBI) database. The method consists of a range of data cleansing and deep neural network tools for big data situation. Findings reveal that the proposed approach is better than other methods in detecting DNA sequence breaks for TB via resolving a sample size issue of the training dataset and recursively divide the whole dataset into certain length to detect the breaks. It also provides a faster predictive analysis with more accurate and reliable outcomes.",

keywords = "Deep neural network, Hidden Markov model, Tuberculosis, proteins",

author = "Azizur Rahman and Nimmy, {S. F.} and G. Sarowar",

year = "2019",

doi = "10.1007/978-3-319-93351-1_23",

language = "English",

isbn = "9783319933504",

pages = "277--286",

editor = "Davim, {J. Paulo}",

booktitle = "Proceedings of the Twelfth International Conference on Management Science and Engineering Management",

publisher = "Springer",

address = "United States",

note = "12th International Conference on Management Science and Engineering Management : ICMSEM 2018 ; Conference date: 01-08-2018 Through 04-08-2018",

url = "http://www.icmsem.org/index.html#past",

}

Rahman, A, Nimmy, SF & Sarowar, G 2019, Developing an automated machine learning approach to test discontinuity in DNA for detecting tuberculosis. in JP Davim (ed.), Proceedings of the Twelfth International Conference on Management Science and Engineering Management. Springer, Cham, Switzerland, pp. 277-286, 12th International Conference on Management Science and Engineering Management, Melbourne, Victoria, Australia, 01/08/18. https://doi.org/10.1007/978-3-319-93351-1_23

Developing an automated machine learning approach to test discontinuity in DNA for detecting tuberculosis. / Rahman, Azizur; Nimmy, S. F. ; Sarowar, G. .
Proceedings of the Twelfth International Conference on Management Science and Engineering Management. ed. / J. Paulo Davim. Cham, Switzerland: Springer, 2019. p. 277-286.

Research output: Book chapter/Published conference paper › Conference paper › peer-review

TY - GEN

T1 - Developing an automated machine learning approach to test discontinuity in DNA for detecting tuberculosis

AU - Rahman, Azizur

AU - Nimmy, S. F.

AU - Sarowar, G.

PY - 2019

Y1 - 2019

N2 - Discontinuity in long DNA sequences creates harmful diseases like Tuberculosis (TB). Given the 21th centurys exponential growth of big-data environments, knowing the precise breaks position of DNA sequences is essential for many reasons including advanced medical intervention. This study designs an automated framework to assess the breaks positions in long DNA sequences which are responsible for TB and then empirically tests it by analyzing a big DNA dataset from the National Center for Biotechnology Information (NCBI) database. The method consists of a range of data cleansing and deep neural network tools for big data situation. Findings reveal that the proposed approach is better than other methods in detecting DNA sequence breaks for TB via resolving a sample size issue of the training dataset and recursively divide the whole dataset into certain length to detect the breaks. It also provides a faster predictive analysis with more accurate and reliable outcomes.

AB - Discontinuity in long DNA sequences creates harmful diseases like Tuberculosis (TB). Given the 21th centurys exponential growth of big-data environments, knowing the precise breaks position of DNA sequences is essential for many reasons including advanced medical intervention. This study designs an automated framework to assess the breaks positions in long DNA sequences which are responsible for TB and then empirically tests it by analyzing a big DNA dataset from the National Center for Biotechnology Information (NCBI) database. The method consists of a range of data cleansing and deep neural network tools for big data situation. Findings reveal that the proposed approach is better than other methods in detecting DNA sequence breaks for TB via resolving a sample size issue of the training dataset and recursively divide the whole dataset into certain length to detect the breaks. It also provides a faster predictive analysis with more accurate and reliable outcomes.

KW - Deep neural network

KW - Hidden Markov model

KW - Tuberculosis

KW - proteins

U2 - 10.1007/978-3-319-93351-1_23

DO - 10.1007/978-3-319-93351-1_23

M3 - Conference paper

SN - 9783319933504

SP - 277

EP - 286

BT - Proceedings of the Twelfth International Conference on Management Science and Engineering Management

A2 - Davim, J. Paulo

PB - Springer

CY - Cham, Switzerland

T2 - 12th International Conference on Management Science and Engineering Management

Y2 - 1 August 2018 through 4 August 2018

ER -

Developing an automated machine learning approach to test discontinuity in DNA for detecting tuberculosis

Abstract

Conference

UN SDGs

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Cite this