Lung Cancer Classification with Discriminant Features of Mutated Genes using Machine Learning
Created by W.Langdon from
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- @PhdThesis{Sattar:thesis,
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author = "Mohsin Sattar",
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title = "Lung Cancer Classification with Discriminant Features
of Mutated Genes using Machine Learning",
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school = "Pakistan Institute of Engineering and Applied
Sciences",
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year = "2019",
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address = "Islamabad, Pakistan",
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keywords = "genetic algorithms, genetic programming",
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URL = "
http://prr.hec.gov.pk/jspui/bitstream/123456789/11487/1/Mohsin%20Sattar%20CS%20year%202019%2003-7P1-002-2014.pdf",
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abstract = "Machine learning based mathematical and statistical
models are employed for the development of improved
classification systems. These decision based systems
have the capability of automatically learning from
complex sequential data. In this work, machine learning
models are developed for the classification of lung
cancer. The early classification of lung cancer is
critical for successful cancer treatment. Genes and
proteins are important in the normal functioning of the
human body. The abnormal processes due to somatic
mutations transform normal cells into cancer cells. The
somatic mutations in genes are ultimately reflected in
gene expression and proteins amino acid sequences.
Influential information is extracted during the
statistical analysis of gene expression and proteins
amino acid sequences data. This information is
transformed into discriminant feature spaces using
physiochemical properties. The machine learning
capability is exploited effectively using discriminant
information of mutated genes in proteomic and genomic
data.This study aims to develop artificial intelligent
lung cancer classification systems. The development was
carried out in three main phases. In the first phase,
lung cancer classification system using protein amino
acid sequences is developed by employing various
individual learning algorithms. In the second phase,
lung cancer classification system using protein amino
acid sequences is developed by employing multi-gene
genetic programming. This approach exploits
evolutionary learning capability by optimally combining
the selected discriminant features with primitive
functions. The third phase is focussed on the
development of improved lung cancer classification
system using influential features of gene expression
with the imbalanced dataset by employing rotation
forest. In the thesis work, extensive experiments are
conducted to evaluate the performance of various lung
cancer classification systems. The proposed systems
have obtained excellent accuracy values in the range of
95%99%. The comparative analysis highlights that
proposed lung cancer classification systems are better
than previous approaches. It is expected that research
outcome would impact in the fields of diagnosis,
prevention, and effective treatment of lung cancer.",
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notes = "Pakistan Research Repository https://prr.hec.gov.pk
› jspui › bitstream › Mohsin...
8 Jul 2019
Language: English
ARI ID: 1676727785199",
- }
Genetic Programming entries for
Mohsin Sattar
Citations