Most recent issue published online in the International Journal of Computational Intelligence in Bioinformatics and Systems Biology.
International Journal of Computational Intelligence in Bioinformatics and Systems Biology
http://www.inderscience.com/browse/index.php?journalID=281&year=2020&vol=2&issue=1
Inderscience Publishers Ltd
en-uk
support@inderscience.com
International Journal of Computational Intelligence in Bioinformatics and Systems Biology
1755-8034
1755-8042
© 2020 Inderscience Enterprises Ltd.
© 2020 Inderscience Publishers Ltd
editor@inderscience.com
International Journal of Computational Intelligence in Bioinformatics and Systems Biology
https://www.inderscience.com/images/files/coverImgs/ijcibsb_scoverijcibsb.jpg
http://www.inderscience.com/browse/index.php?journalID=281&year=2020&vol=2&issue=1
-
Discovering of gapped motifs using particle swarm optimisation
http://www.inderscience.com/link.php?id=106858
In bioinformatics, motif discovery is one of the fundamental and important computational problems. Identifying these recurring patterns in biological sequences helps us to better understand the mechanisms that regulate gene expression. Recently several evolutionary algorithms have been developed to solve motif discovery problem, because of their efficiency in searching multidimensional solution space. HPSO, IPSO-GA, PMbPSO and PSO+ are based on particle swarm optimisation (PSO) algorithms. Among these, PSO+ is the first one to be proposed for finding gapped motifs. PSO+ is less efficient in finding gapped motifs that are located at the centre of a motif. Here, our contribution is, to find gapped motifs that are present at the centre of two conserved regions efficiently by adopting features of PSO to solve the problem. We performed experiments on simulated and real biological datasets. It is observed that our approach is able to detect known gapped TFBS more accurately and efficiently.
Discovering of gapped motifs using particle swarm optimisation
Uyyala Srinivasulu Reddy; Michael Arock; A.V. Reddy
International Journal of Computational Intelligence in Bioinformatics and Systems Biology, Vol. 2, No. 1 (2020) pp. 1 - 21
In bioinformatics, motif discovery is one of the fundamental and important computational problems. Identifying these recurring patterns in biological sequences helps us to better understand the mechanisms that regulate gene expression. Recently several evolutionary algorithms have been developed to solve motif discovery problem, because of their efficiency in searching multidimensional solution space. HPSO, IPSO-GA, PMbPSO and PSO+ are based on particle swarm optimisation (PSO) algorithms. Among these, PSO+ is the first one to be proposed for finding gapped motifs. PSO+ is less efficient in finding gapped motifs that are located at the centre of a motif. Here, our contribution is, to find gapped motifs that are present at the centre of two conserved regions efficiently by adopting features of PSO to solve the problem. We performed experiments on simulated and real biological datasets. It is observed that our approach is able to detect known gapped TFBS more accurately and efficiently.]]>
10.1504/IJCIBSB.2020.106858
International Journal of Computational Intelligence in Bioinformatics and Systems Biology, Vol. 2, No. 1 (2020) pp. 1 - 21
Uyyala Srinivasulu Reddy
Michael Arock
A.V. Reddy
Department of Computer Applications, National Institute of Technology, Tiruchirappalli 620015, India ' Department of Computer Applications, National Institute of Technology, Tiruchirappalli 620015, India ' Department of Computer Applications, National Institute of Technology, Tiruchirappalli 620015, India
evolutionary optimisation bioinformatics
computational biology
techniques
gene regulation
motif finding
particle swarm optimisation
PSO
swarm intelligence
SI
transcriptional factor binding sites
TFBS
gapped motifs
2020-04-24T23:20:50-05:00
Copyright © 2020 Inderscience Enterprises Ltd.
2
1
1
21
2020-04-24T23:20:50-05:00
-
SMISS: a protein function prediction server by integrating multiple sources
http://www.inderscience.com/link.php?id=106859
SMISS is a novel web server for protein function prediction. Three different predictors can be selected for different usage. It integrates different sources to improve the protein function prediction accuracy, including the query protein sequence, protein-protein interaction network, gene-gene interaction network and the rules mined from protein function associations. SMISS automatically switch to ab initio protein function prediction based on the query sequence when there is no homolog's in the database. It takes fasta format sequences as input; and several sequences can be submitted together without influencing the computation speed too much. PHP and Perl are two primary programming language used in the server. The CodeIgniter MVC PHP web framework and bootstrap front-end framework are used for building the server. It can be used in different platforms in standard web browser, such as Windows, Mac OS X, Linux and iOS. No plug-ins is needed for our website (availability: http://tulip.rnet.missouri.edu/profunc/).
SMISS: a protein function prediction server by integrating multiple sources
Renzhi Cao; Zhaolong Zhong; Jianlin Cheng
International Journal of Computational Intelligence in Bioinformatics and Systems Biology, Vol. 2, No. 1 (2020) pp. 22 - 30
SMISS is a novel web server for protein function prediction. Three different predictors can be selected for different usage. It integrates different sources to improve the protein function prediction accuracy, including the query protein sequence, protein-protein interaction network, gene-gene interaction network and the rules mined from protein function associations. SMISS automatically switch to ab initio protein function prediction based on the query sequence when there is no homolog's in the database. It takes fasta format sequences as input; and several sequences can be submitted together without influencing the computation speed too much. PHP and Perl are two primary programming language used in the server. The CodeIgniter MVC PHP web framework and bootstrap front-end framework are used for building the server. It can be used in different platforms in standard web browser, such as Windows, Mac OS X, Linux and iOS. No plug-ins is needed for our website (availability: http://tulip.rnet.missouri.edu/profunc/).]]>
10.1504/IJCIBSB.2020.106859
International Journal of Computational Intelligence in Bioinformatics and Systems Biology, Vol. 2, No. 1 (2020) pp. 22 - 30
Renzhi Cao
Zhaolong Zhong
Jianlin Cheng
Pacific Lutheran University, 12180 Park Ave S, Tacoma, WA 98447, USA ' University of Missouri-Columbia, 230 Jesse Hall, Columbia, MO 65211, USA ' University of Missouri-Columbia, 230 Jesse Hall, Columbia, MO 65211, USA
protein function prediction
data integration
spatial gene-gene interaction network
protein-protein interaction network
chromosome conformation capturing
2020-04-24T23:20:50-05:00
Copyright © 2020 Inderscience Enterprises Ltd.
2
1
22
30
2020-04-24T23:20:50-05:00
-
Ortholog detection: pathway to comparative genomics
http://www.inderscience.com/link.php?id=106860
Accurate detection of orthologs is a key aspect of comparative genomics. Orthologs can be used to predict the function of newly sequenced genes from the model organisms as they retain the same biological function through the path of evolution. In this paper, we describe different methods available for the detection of orthologs. Different computational methods comprising of phylogenetic as well as pair-wise comparison are discussed and compared. Some other methods based on synteny and protein network comparisons are also included in the paper. The study shows that phylogenetic methods of detecting orthologs are comparatively accurate and reliable than the pair-wise graph based methods but computationally more intensive and slow. These should be used only when sufficient computational power is available for operation. On the other hand, pair-wise approaches are fast and can handle large amounts of data. Synteny based methods also form a good candidate for the detection of orthologs.
Ortholog detection: pathway to comparative genomics
Manpreet Singh; Shaifu Gupta
International Journal of Computational Intelligence in Bioinformatics and Systems Biology, Vol. 2, No. 1 (2020) pp. 31 - 47
Accurate detection of orthologs is a key aspect of comparative genomics. Orthologs can be used to predict the function of newly sequenced genes from the model organisms as they retain the same biological function through the path of evolution. In this paper, we describe different methods available for the detection of orthologs. Different computational methods comprising of phylogenetic as well as pair-wise comparison are discussed and compared. Some other methods based on synteny and protein network comparisons are also included in the paper. The study shows that phylogenetic methods of detecting orthologs are comparatively accurate and reliable than the pair-wise graph based methods but computationally more intensive and slow. These should be used only when sufficient computational power is available for operation. On the other hand, pair-wise approaches are fast and can handle large amounts of data. Synteny based methods also form a good candidate for the detection of orthologs.]]>
10.1504/IJCIBSB.2020.106860
International Journal of Computational Intelligence in Bioinformatics and Systems Biology, Vol. 2, No. 1 (2020) pp. 31 - 47
Manpreet Singh
Shaifu Gupta
Department of Information Technology, Guru Nanak Dev Engineering College, Ludhiana, India ' Department of Computer Science and Engineering, Guru Nanak Dev Engineering College, Ludhiana, India
orthologs
evolution
phylogenetic methods
comparative genomics
pair-wise methods
synteny
2020-04-24T23:20:50-05:00
Copyright © 2020 Inderscience Enterprises Ltd.
2
1
31
47
2020-04-24T23:20:50-05:00
-
Combining associative classification with multifactor dimensionality reduction for predicting higher-order SNP interactions in case-control studies
http://www.inderscience.com/link.php?id=106861
The identification and characterisation of genotype-phenotype mapping is a central focus of current genome wide association interaction studies (GWAIS). Revealing these relationships for exposing the hidden structures of diseases has received considerable attention by a number of researchers. However, the current statistical and computational approaches ignore many complex genetic contexts. A multifactor dimensionality reduction based on associative classification was previously proposed for detecting multi-locus single nucleotide polymorphism (SNP) interactions in GWAIS. The approach is further studied in detail by adjusting threshold levels, and adding noise to the datasets. The simulated studies demonstrated significant improvements in accuracy by adjusting threshold values over the previous approaches. The results also indicate that the approach is robust in the presence of noise. Further, the application of this approach to real world data has demonstrated higher-order interactions among five SNPs for the manifestation of breast cancer, and three SNPs for the manifestation of hypertension.
Combining associative classification with multifactor dimensionality reduction for predicting higher-order SNP interactions in case-control studies
Suneetha Uppu; Aneesh Krishna; Raj P. Gopalan
International Journal of Computational Intelligence in Bioinformatics and Systems Biology, Vol. 2, No. 1 (2020) pp. 48 - 84
The identification and characterisation of genotype-phenotype mapping is a central focus of current genome wide association interaction studies (GWAIS). Revealing these relationships for exposing the hidden structures of diseases has received considerable attention by a number of researchers. However, the current statistical and computational approaches ignore many complex genetic contexts. A multifactor dimensionality reduction based on associative classification was previously proposed for detecting multi-locus single nucleotide polymorphism (SNP) interactions in GWAIS. The approach is further studied in detail by adjusting threshold levels, and adding noise to the datasets. The simulated studies demonstrated significant improvements in accuracy by adjusting threshold values over the previous approaches. The results also indicate that the approach is robust in the presence of noise. Further, the application of this approach to real world data has demonstrated higher-order interactions among five SNPs for the manifestation of breast cancer, and three SNPs for the manifestation of hypertension.]]>
10.1504/IJCIBSB.2020.106861
International Journal of Computational Intelligence in Bioinformatics and Systems Biology, Vol. 2, No. 1 (2020) pp. 48 - 84
Suneetha Uppu
Aneesh Krishna
Raj P. Gopalan
School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Kent Street, Bentley, WA-6102, Australia ' School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Kent Street, Bentley, WA-6102, Australia ' School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Kent Street, Bentley, WA-6102, Australia
epistasis
SNP-SNP interactions
multifactor dimensionality reduction based associative classification
multi-locus interactions
data mining and machine learning approaches
2020-04-24T23:20:50-05:00
Copyright © 2020 Inderscience Enterprises Ltd.
2
1
48
84
2020-04-24T23:20:50-05:00