Lung cancer prediction using multi-gene genetic programming by selecting automatic features from amino acid sequences
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{SATTAR:2022:CBC,
-
author = "Mohsin Sattar and Abdul Majid and Nabeela Kausar and
Muhammad Bilal and Muhammad Kashif",
-
title = "Lung cancer prediction using multi-gene genetic
programming by selecting automatic features from amino
acid sequences",
-
journal = "Computational Biology and Chemistry",
-
pages = "107638",
-
year = "2022",
-
ISSN = "1476-9271",
-
DOI = "doi:10.1016/j.compbiolchem.2022.107638",
-
URL = "https://www.sciencedirect.com/science/article/pii/S1476927122000184",
-
keywords = "genetic algorithms, genetic programming, Lung cancer
prediction, multi-gene genetic programming, protein
amino acid sequence, gene mutation, somatic mutation",
-
abstract = "Lung cancer is one of the leading causes of cancer
related deaths. Early diagnosis of lung cancer using
automatic feature selection from large number of
features is a challenging task. Conventionally, cancer
diagnosis approaches use physical features that appear
in later stages, while harmful effects have already
been occurred due to abnormal somatic mutations. In
order to extract useful novel patterns to efficiently
predict cancer at early stages, we analyzed lung cancer
related mutated genes that reveal useful information in
protein amino acid sequences. For this, we developed a
new evolutionary learning technique with biologically
inspired multi-gene genetic programming algorithm using
discriminant information of protein amino acids. The
proposed model efficiently selects 23 discriminant
features out of 1500 features. Then it combines the
selected features and related primitive functions
optimally for prediction of lung cancer. Hence, an
efficient predictive model is constructed that helps in
understanding the complex heterogeneous nature of lung
cancer. The proposed system achieved area under ROC
curve and accuracy values of 98.79percent and
95.67percent, respectively outperforming related lung
cancer prediction approaches",
- }
Genetic Programming entries for
Mohsin Sattar
Abdul Majid
Nabeela Kausar
Muhammad Bilal
Muhammad Kashif
Citations