ITIS-PseKNC : Identification of Translation Initiation Site in human genes using pseudo k-tuple nucleotides composition
(2015) In Computers in Biology and Medicine 66. p.252-257- Abstract
Translation is an essential genetic process for understanding the mechanism of gene expression. Due to the large number of protein sequences generated in the post-genomic era, conventional methods are unable to identify Translation Initiation Site (TIS) in human genes timely and accurately. It is thus highly desirable to develop an automatic and accurate computational model for identification of TIS. Considerable improvements have been achieved in developing computational models; however, development of accurate and reliable automated systems for TIS identification in human genes is still a challenging task. In this connection, we propose iTIS-PseKNC, a novel protocol for identification of TIS. Three protein sequence representation... (More)
Translation is an essential genetic process for understanding the mechanism of gene expression. Due to the large number of protein sequences generated in the post-genomic era, conventional methods are unable to identify Translation Initiation Site (TIS) in human genes timely and accurately. It is thus highly desirable to develop an automatic and accurate computational model for identification of TIS. Considerable improvements have been achieved in developing computational models; however, development of accurate and reliable automated systems for TIS identification in human genes is still a challenging task. In this connection, we propose iTIS-PseKNC, a novel protocol for identification of TIS. Three protein sequence representation methods including dinucleotide composition, pseudo-dinucleotide composition and Trinucleotide composition have been used in order to extract numerical descriptors. Support Vector Machine (SVM), K-nearest neighbor and Probabilistic Neural Network are assessed for their performance using the constructed descriptors. The proposed model iTIS-PseKNC has achieved 99.40% accuracy using jackknife test. The experimental results validated the superior performance of iTIS-PseKNC over the existing methods reported in the literature. It is highly anticipated that the iTIS-PseKNC predictor will be useful for basic research studies.
(Less)
- author
- Kabir, Muhammad LU ; Iqbal, Muhammad ; Ahmad, Saeed and Hayat, Maqsood
- publishing date
- 2015-11-01
- type
- Contribution to journal
- publication status
- published
- keywords
- DNA, DNC, Jackknife test, PseDNC, SVM, Translation Initiation Site
- in
- Computers in Biology and Medicine
- volume
- 66
- pages
- 252 - 257
- publisher
- Elsevier
- external identifiers
-
- pmid:26433457
- scopus:84943169413
- ISSN
- 0010-4825
- DOI
- 10.1016/j.compbiomed.2015.09.010
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2015 Elsevier Ltd.
- id
- c2ead3cf-3fd0-4ae0-a862-d431bf038b7d
- date added to LUP
- 2024-07-03 11:37:17
- date last changed
- 2024-07-04 09:35:08
@article{c2ead3cf-3fd0-4ae0-a862-d431bf038b7d,
abstract = {{<p>Translation is an essential genetic process for understanding the mechanism of gene expression. Due to the large number of protein sequences generated in the post-genomic era, conventional methods are unable to identify Translation Initiation Site (TIS) in human genes timely and accurately. It is thus highly desirable to develop an automatic and accurate computational model for identification of TIS. Considerable improvements have been achieved in developing computational models; however, development of accurate and reliable automated systems for TIS identification in human genes is still a challenging task. In this connection, we propose iTIS-PseKNC, a novel protocol for identification of TIS. Three protein sequence representation methods including dinucleotide composition, pseudo-dinucleotide composition and Trinucleotide composition have been used in order to extract numerical descriptors. Support Vector Machine (SVM), K-nearest neighbor and Probabilistic Neural Network are assessed for their performance using the constructed descriptors. The proposed model iTIS-PseKNC has achieved 99.40% accuracy using jackknife test. The experimental results validated the superior performance of iTIS-PseKNC over the existing methods reported in the literature. It is highly anticipated that the iTIS-PseKNC predictor will be useful for basic research studies.</p>}},
author = {{Kabir, Muhammad and Iqbal, Muhammad and Ahmad, Saeed and Hayat, Maqsood}},
issn = {{0010-4825}},
keywords = {{DNA; DNC; Jackknife test; PseDNC; SVM; Translation Initiation Site}},
language = {{eng}},
month = {{11}},
pages = {{252--257}},
publisher = {{Elsevier}},
series = {{Computers in Biology and Medicine}},
title = {{ITIS-PseKNC : Identification of Translation Initiation Site in human genes using pseudo k-tuple nucleotides composition}},
url = {{http://dx.doi.org/10.1016/j.compbiomed.2015.09.010}},
doi = {{10.1016/j.compbiomed.2015.09.010}},
volume = {{66}},
year = {{2015}},
}