International Journal of Computational Biology and Drug Design http://www.inderscience.com/browse/index.php?journalID=294&year=2011&vol=4&issue=4 Inderscience Publishers Ltd en-uk International Journal of Computational Biology and Drug Design 1756-0756 1756-0764 © 2011 Inderscience Publishers Ltd editor@inderscience.com https://www.inderscience.com/images/files/coverImgs/ijcbdd_scoverijcbdd.jpg http://www.inderscience.com/browse/index.php?journalID=294&year=2011&vol=4&issue=4 http://www.inderscience.com/link.php?id=44443 Analysis of gene expression microarray datasets presents the high risk of over-fitting (spurious patterns) because of their feature-rich but case-poor nature. This paper describes our ongoing efforts to develop a method to combat over-fitting and determine the strongest signal in the dataset. A GA-SVM hybrid along with Gaussian noise (manual noise gain) is used to discover feature sets of minimal size that accurately classifies the cases under cross-validation. Initial results on a colorectal cancer dataset shows that the strongest signal (modest number of candidates) can be found by a binary search. Perturbation and candidate analysis to combat overfitting of gene expression microarray data
Ravi Mathur; J. David Schaffer; Walker H. Land <suffix>Jr.</suffic>; John J. Heine; Jonathan M. Hernandez; Timothy Yeatman
International Journal of Computational Biology and Drug Design, Vol. 4, No. 4 (2011) pp. 307 - 315
Analysis of gene expression microarray datasets presents the high risk of over-fitting (spurious patterns) because of their feature-rich but case-poor nature. This paper describes our ongoing efforts to develop a method to combat over-fitting and determine the strongest signal in the dataset. A GA-SVM hybrid along with Gaussian noise (manual noise gain) is used to discover feature sets of minimal size that accurately classifies the cases under cross-validation. Initial results on a colorectal cancer dataset shows that the strongest signal (modest number of candidates) can be found by a binary search.

]]> 10.1504/IJCBDD.2011.044443 International Journal of Computational Biology and Drug Design, Vol. 4, No. 4 (2011) pp. 307 - 315 Ravi Mathur; J. David Schaffer; Walker H. Land <suffix>Jr.</suffic>; John J. Heine; Jonathan M. Hernandez; Timothy Yeatman Department of Bioengineering, Binghamton University, Binghamton, NY 13902, USA. ' Department of Bioengineering, Binghamton University, Binghamton, NY 13902, USA. ' Department of Bioengineering, Binghamton University, Binghamton, NY 13902, USA. ' H. Lee Moffitt Cancer Center & Research Institute and University of South Florida, Tampa, FL 33620, USA. ' H. Lee Moffitt Cancer Center & Research Institute and University of South Florida, Tampa, FL 33620, USA. ' H. Lee Moffitt Cancer Center & Research Institute and University of South Florida, Tampa, FL 33620, USA Az value colorectal cancer cross-validation DNA microarray GAs genetic algorithms noise perturbation overfitting ROC curve support vector machines SVM gene expression microarray data. 2011-12-24T23:20:50-05:00 4 4 307 315 2011-12-24T23:20:50-05:00 http://www.inderscience.com/link.php?id=44446 Recent advancements of the next-generation sequencing technology have enabled the direct sequencing of rare genetic variants in both case and control individuals. Although there have been a few statistical methods for uncovering potential associations between multiple rare variants and human inherited diseases, most of these methods require computational approaches to filter out non-functional variants for the purpose of maximising the statistical power. To tackle this problem, we formulate the detection of genetic variants that are associated with a specific type of disease from the perspective of one-class novelty learning. We focus on a typical type of genetic variants called Single Amino Acid Polymorphisms (SAAPs), and we take advantages of a feature selection mechanism and two one-class learning methods to prioritise candidate SAAPs. Systematic validation demonstrates that the proposed model is effective in recovering disease-associated SAAPs. Prioritisation of candidate Single Amino Acid Polymorphisms using one-class learning machines
Jiaxin Wu; Mingxin Gan; Rui Jiang
International Journal of Computational Biology and Drug Design, Vol. 4, No. 4 (2011) pp. 316 - 331
Recent advancements of the next-generation sequencing technology have enabled the direct sequencing of rare genetic variants in both case and control individuals. Although there have been a few statistical methods for uncovering potential associations between multiple rare variants and human inherited diseases, most of these methods require computational approaches to filter out non-functional variants for the purpose of maximising the statistical power. To tackle this problem, we formulate the detection of genetic variants that are associated with a specific type of disease from the perspective of one-class novelty learning. We focus on a typical type of genetic variants called Single Amino Acid Polymorphisms (SAAPs), and we take advantages of a feature selection mechanism and two one-class learning methods to prioritise candidate SAAPs. Systematic validation demonstrates that the proposed model is effective in recovering disease-associated SAAPs.

]]> 10.1504/IJCBDD.2011.044446 International Journal of Computational Biology and Drug Design, Vol. 4, No. 4 (2011) pp. 316 - 331 Jiaxin Wu; Mingxin Gan; Rui Jiang MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China. ' School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China. ' MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China rare variants SAAPs single amino acid polymorphisms one-class SVMs support vector machines Parzen probabilistic neural networks principal component analysis PCA sequencing genetic variants feature selection. 2011-12-24T23:20:50-05:00 4 4 316 331 2011-12-24T23:20:50-05:00 http://www.inderscience.com/link.php?id=44395 Citronella oil is one of the essential oils obtained from Cymbopogon sp. having medicinally important aromatic chemicals (like citronellal, citronellol, hydroxy-citronellol and geraniol) exhibiting insecticidal, anti-oxidant and anti-inflammatory effects. Geraniol Dehydrogenase (GDH) is responsible for the degradation of Citronella oil. Therefore, we aimed to generate 3D structure of GDH and a potent specific GDH inhibitor by homology modelling, virtual screening of ligand database and molecular docking. Inhibitor model indicated strong binding affinity to the binding pocket of GDH and varying affinity for different ligands. Obtained structures will open the possibility of testing new inhibitor families, in addition to new substituent for the already known lead structures. In silico analysis of enzyme involved in enrichment of citronella oil
Divya Sahu; Ashutosh Kumar; D.M. Pandey
International Journal of Computational Biology and Drug Design, Vol. 4, No. 4 (2011) pp. 332 - 344
Citronella oil is one of the essential oils obtained from Cymbopogon sp. having medicinally important aromatic chemicals (like citronellal, citronellol, hydroxy-citronellol and geraniol) exhibiting insecticidal, anti-oxidant and anti-inflammatory effects. Geraniol Dehydrogenase (GDH) is responsible for the degradation of Citronella oil. Therefore, we aimed to generate 3D structure of GDH and a potent specific GDH inhibitor by homology modelling, virtual screening of ligand database and molecular docking. Inhibitor model indicated strong binding affinity to the binding pocket of GDH and varying affinity for different ligands. Obtained structures will open the possibility of testing new inhibitor families, in addition to new substituent for the already known lead structures.

]]> 10.1504/IJCBDD.2011.044395 International Journal of Computational Biology and Drug Design, Vol. 4, No. 4 (2011) pp. 332 - 344 Divya Sahu; Ashutosh Kumar; D.M. Pandey Department of Biotechnology, Birla Institute of Technology, Mesra, Ranchi 835215, Jharkhand, India. ' Department of Biotechnology, Birla Institute of Technology, Mesra, Ranchi 835215, Jharkhand, India. ' Department of Biotechnology, Birla Institute of Technology, Mesra, Ranchi 835215, Jharkhand, India citronella oil GDH inhibitors geraniol dehydrogenase cinnamyl alcohol dehydrogenase homology modelling modeller molecular docking GLIDE GEMDOCK aromatic chemicals inhibitor families GDH structure 3D modelling. 2011-12-24T23:20:50-05:00 4 4 332 344 2011-12-24T23:20:50-05:00 http://www.inderscience.com/link.php?id=44398 Quantitative assessment of structural connectivities between cortical and subcortical regions has been of increasing interest in recent years. This paper proposes an algorithmic pipeline for identification of reliable cortical landmarks based on the consistent structural connectivity patterns between cortical and subcortical regions. Our experimental results of eight healthy subjects show that reliable and meaningful cortical landmarks can be extracted by using our approaches. Furthermore, subcortical regions can serve as reliable reference points for the identification of consistent corresponding cortical regions across individuals. Identification of cortical landmarks based on structural connectivity to subcortical regions
Degang Zhang; Lei Guo; Xintao Hu; Kaiming Li; Dajiang Zhu; Xi Jiang; Hanbo Chen; Fan Deng; Qun Zhao; Tianming Liu
International Journal of Computational Biology and Drug Design, Vol. 4, No. 4 (2011) pp. 345 - 360
Quantitative assessment of structural connectivities between cortical and subcortical regions has been of increasing interest in recent years. This paper proposes an algorithmic pipeline for identification of reliable cortical landmarks based on the consistent structural connectivity patterns between cortical and subcortical regions. Our experimental results of eight healthy subjects show that reliable and meaningful cortical landmarks can be extracted by using our approaches. Furthermore, subcortical regions can serve as reliable reference points for the identification of consistent corresponding cortical regions across individuals.

]]> 10.1504/IJCBDD.2011.044398 International Journal of Computational Biology and Drug Design, Vol. 4, No. 4 (2011) pp. 345 - 360 Degang Zhang; Lei Guo; Xintao Hu; Kaiming Li; Dajiang Zhu; Xi Jiang; Hanbo Chen; Fan Deng; Qun Zhao; Tianming Liu School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China; Department of Physics and Bioimaging Research Center, The University of Georgia, Athens, GA 30605, USA ' School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China ' School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China ' School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China; Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA 30605, USA ' Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, 30605, USA ' Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA 30605, USA ' Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA 30605, USA ' Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA 30605, USA ' Department of Physics and Bioimaging Research Center, The University of Georgia, Athens, GA 30605, USA ' Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA 30605, USA cortical surface parcellation subcortical regions connectivity patterns cortical landmarks structural connectivities. 2011-12-24T23:20:50-05:00 4 4 345 360 2011-12-24T23:20:50-05:00 http://www.inderscience.com/link.php?id=44444 Cellular behaviour depends on and also modifies protein concentration and activity. An integrated cellular and gene interaction model is proposed to reveal this relationship. In this model, protein activity varies spatiotemporally with cellular location, gene interaction, and diffusion. In the meanwhile, cellular behaviour can vary spatially, driven by cell-cell signalling and inhomogeneous protein distribution across cells. This model integrates two components. The first component adopts a variation of the reaction-diffusion mechanism at the gene expression level. The second component is a lattice cellular model based on the Differential Adhesion Hypothesis (DAH) for cell sorting at the cellular level. Cell sorting and tumour invasion were simulated to illustrate the model. This model approximates cellular pattern formation more closely than existing models based on cell density. Integrated cellular and gene interaction modelling of pattern formation
Hien Nguyen; Mingzhou Song
International Journal of Computational Biology and Drug Design, Vol. 4, No. 4 (2011) pp. 361 - 372
Cellular behaviour depends on and also modifies protein concentration and activity. An integrated cellular and gene interaction model is proposed to reveal this relationship. In this model, protein activity varies spatiotemporally with cellular location, gene interaction, and diffusion. In the meanwhile, cellular behaviour can vary spatially, driven by cell-cell signalling and inhomogeneous protein distribution across cells. This model integrates two components. The first component adopts a variation of the reaction-diffusion mechanism at the gene expression level. The second component is a lattice cellular model based on the Differential Adhesion Hypothesis (DAH) for cell sorting at the cellular level. Cell sorting and tumour invasion were simulated to illustrate the model. This model approximates cellular pattern formation more closely than existing models based on cell density.

]]> 10.1504/IJCBDD.2011.044444 International Journal of Computational Biology and Drug Design, Vol. 4, No. 4 (2011) pp. 361 - 372 Hien Nguyen; Mingzhou Song Department of Computer Science, New Mexico State University, Las Cruces, NM 88003, USA. ' Department of Computer Science, New Mexico State University, Las Cruces, NM 88003, USA multi-scale modelling cell adhesion cell migration pattern formation gene interaction modelling cellular interaction modelling protein activity cellular location cell sorting tumour invasion. 2011-12-24T23:20:50-05:00 4 4 361 372 2011-12-24T23:20:50-05:00 http://www.inderscience.com/link.php?id=44404 Gankyrin is an oncoprotein composed of six ankyrin repeats, over-expressed in the Hepatocellular Carcinoma (HCC), and directly involved in the cell cycle regulation. Therefore, it is a potential drug target to restrict the growth of cancer cell and activation of apoptosis. We have successfully designed a potent ligand to inhibit the activity of gankyrin. Using docking approach we designed a potential ligand, which is exactly fitting in the cavity of gankyrin and forming many close interactions to protein atoms including its active site residues. This molecule shows minimum energy and good binding affinity for gankyrin. Discovering a potent small molecule inhibitor for gankyrin using de novo drug design approach
Prasoon Kumar Thakur; Md. Imtaiyaz Hassan
International Journal of Computational Biology and Drug Design, Vol. 4, No. 4 (2011) pp. 373 - 386
Gankyrin is an oncoprotein composed of six ankyrin repeats, over-expressed in the Hepatocellular Carcinoma (HCC), and directly involved in the cell cycle regulation. Therefore, it is a potential drug target to restrict the growth of cancer cell and activation of apoptosis. We have successfully designed a potent ligand to inhibit the activity of gankyrin. Using docking approach we designed a potential ligand, which is exactly fitting in the cavity of gankyrin and forming many close interactions to protein atoms including its active site residues. This molecule shows minimum energy and good binding affinity for gankyrin.

]]> 10.1504/IJCBDD.2011.044404 International Journal of Computational Biology and Drug Design, Vol. 4, No. 4 (2011) pp. 373 - 386 Prasoon Kumar Thakur; Md. Imtaiyaz Hassan Department of Computer Science, Jamia Millia Islamia, Jamia Nagar, New Delhi 110025, India. ' Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, Jamia Nagar, New Delhi 110025, India HCC hepatocellular carcinoma ubiquitination oncoprotein apoptosis cyclin-dependent kinase 4 proteasome endopeptidase complex retinoblastoma protein molecule inhibitors gankyrin drug design cancer cells. 2011-12-24T23:20:50-05:00 4 4 373 386 2011-12-24T23:20:50-05:00 http://www.inderscience.com/link.php?id=44445 Do steady-state protein levels accurately predict mRNA levels? Based on the central dogma (DNA RNA protein) current protein levels are representative of mRNA present at an earlier time. However, most cellular mRNA protein comparative studies try to relate steady-state protein levels to current mRNA levels in cells. Protein steady-states are more correctly related to protein production, protein degradation and other complex cellular conditions. Using Structural Equation Modelling (SEM) we relate linear protein measurements to latent mRNA in E.coli. This method can be used to find the optimal protein measurements that explain underlying mRNA expression, and better understand the proteomic and transcriptomic relationship in E.coli gene expression. Using protein abundance to indicate underlying mRNA expression levels in E.coli: an SEM modelling approach
Jacqueline J. Harris; Christine W. Duarte; Michael C. Mossing
International Journal of Computational Biology and Drug Design, Vol. 4, No. 4 (2011) pp. 387 - 395
Do steady-state protein levels accurately predict mRNA levels? Based on the central dogma (DNA RNA protein) current protein levels are representative of mRNA present at an earlier time. However, most cellular mRNA protein comparative studies try to relate steady-state protein levels to current mRNA levels in cells. Protein steady-states are more correctly related to protein production, protein degradation and other complex cellular conditions. Using Structural Equation Modelling (SEM) we relate linear protein measurements to latent mRNA in E.coli. This method can be used to find the optimal protein measurements that explain underlying mRNA expression, and better understand the proteomic and transcriptomic relationship in E.coli gene expression.

]]> 10.1504/IJCBDD.2011.044445 International Journal of Computational Biology and Drug Design, Vol. 4, No. 4 (2011) pp. 387 - 395 Jacqueline J. Harris; Christine W. Duarte; Michael C. Mossing Department of Biostatistics, Section on Statistical Genetics, University of Alabama at Birmingham, Birmingham, Alabama 35294, USA. ' Department of Biostatistics, Section on Statistical Genetics, University of Alabama at Birmingham, Birmingham, Alabama 35294, USA. ' Departments of Chemistry and Biochemistry, University of Mississippi Mississippi 38677, USA systems biology SEM structural equation modelling gene expression proteomics transcriptomic relationship protein abundance mRNA expression levels E.coli protein measurements. 2011-12-24T23:20:50-05:00 4 4 387 395 2011-12-24T23:20:50-05:00