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Computational Analysis of gene expression data is extremely difficult, due to the existence of a huge number of genes and less number of samples (limited number of patients). Thus,it is of significant importance to provide a subset of the most informative genesto a learning algorithm, for constructing robust prediction models. In this study, we propose a hybrid Intelligent Water Drop (IWD) - Support Vector Machines (SVM) algorithm, with weighted gene ranking as a heuristic, for simultaneous gene subset selection and cancer prediction. Our results, evaluated on three cancer datasets, demonstrate that the genes selected by the IWD technique yield classification accuracies comparable to previously reported algorithms. © Springer-Verlag 2013.

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Simultaneous gene selection and cancer classification using a hybrid intelligent water drop approach

The equilibrium geometry and electronic structure of graphene on the most stable In-vacancy InAs(111)A surface has been investigated using the density functional and pseudopotential theories. The equilibrium distance between graphene and InAs(111) is found to be 3.05 with adsorption energy approximately 38meV/C atom. According to our electronic band calculation, there is a re-distribution of the charge density around the graphene sheet, which leads to the development of a dipole moment along the surface normal. Scanning tunnelling microscopy image simulations suggest that the InAs(111) substrate is visible through the graphene layer. {\textcopyright} 2012 IOP Publishing Ltd.

Journal of Physics Condensed Matter

Ab initio investigation of the electronic properties of graphene on InAs(111)A

The evolution of atomic and electronic structure of small Au n (n = 1-16 and 55) clusters doped with a Gd atom has been investigated using density functional theory within generalized gradient approximation for the exchange-correlation energy. Pure gold neutral clusters with n up to 15 are planar. However, with the doping of a Gd atom, the atomic structure of gold clusters changes and there is a transition from planar-like structures to three dimensional structures at n = 10. The electronic structure of Gd-doped gold clusters shows a sharp increase in the highest occupied-lowest unoccupied molecular orbital (HOMO-LUMO) gap for certain sizes giving rise to their magic behavior. All clusters are magnetic with large magnetic moments ranging from 6 to 8 $\mu$B primarily due to the localized 4f electrons on Gd. This makes such clusters with large HOMO-LUMO gaps magnetic superatoms. The main interaction between gold and gadolinium atoms in the clusters is due to hybridization between Au-6s and Gd-5d6s orbitals. Our results indicate the emergence of a wheel structure for Gd@Au7, a symmetric cage structure at n = 15 for Gd@Au15 and n = 16 for Gd@Au16+ and Eu@Au16 corresponding to an electronic shell closing at 18 valence electrons leaving aside the f electrons on Gd while for Gd-doped Au 55 corresponding to 58 valence electrons, a Au9Gd@Au 46 core-shell structure is obtained in which the Gd atom connects the core of Au9 with the Au46 shell. The binding energy shows odd-even oscillations with enhancement due to Gd doping compared with pure gold clusters. Such magnetic clusters of gold could have multifunctional biological applications in drug delivery, sensor, imaging and cancer treatment. {\textcopyright} 2012 Springer Science+Business Media, LLC.

Journal of Materials Science

Evolution of atomic and electronic structure of magnetic Gd-doped gold clusters

Microarray cancer gene expression datasets consist of high dimensional data. Gene selection helps in the removal of irrelevant genes. The reduced dimensions of the datasets help in improving the overall classification performance. We present two hybrid techniques, Ant Colony Optimization-AntMiner (ACO-AM) and ACO-RandomForests (ACO-RF) with weighted gene ranking as heuristics. The heuristic information is obtained by a weighted sum of the Information Gain, Chi-Square, Correlation based Feature Selection (CFS) and Gini Index scores for each gene. The ACO algorithm selects a small subset of relevant genes from this ranking. The fitness's of these subsets are then assessed by the cAnt-Miner and the Random Forest classifiers. The performances of the algorithms are tested using two cancer gene expression datasets retrieved from the Kent Ridge Bio-medical Dataset Repository. We demonstrate that genes selected by the suggested algorithms yield better classification accuracies. © 2012 Springer-Verlag GmbH Berlin Heidelberg.

Advances in Intelligent and Soft Computing

Simultaneous informative gene extraction and cancer classification using ACO-AntMiner and ACO-random forests

Microarray cancer gene expression data comprise of very high dimensions. Reducing the dimensions helps in improving the overall analysis and classification performance. We propose two hybrid techniques, Biogeography - based Optimization - Random Forests (BBO - RF) and BBO - SVM (Support Vector Machines) with gene ranking as a heuristic, for microarray gene expression analysis. This heuristic is obtained from information gain filter ranking procedure. The BBO algorithm generates a population of candidate subset of genes, as part of an ecosystem of habitats, and employs the migration and mutation processes across multiple generations of the population to improve the classification accuracy. The fitness of each gene subset is assessed by the classifiers - SVM and Random Forests. The performances of these hybrid techniques are evaluated on three cancer gene expression datasets retrieved from the Kent Ridge Biomedical datasets collection and the libSVM data repository. Our results demonstrate that genes selected by the proposed techniques yield classification accuracies comparable to previously reported algorithms. © 2012 IEEE.

2012 IEEE Congress on Evolutionary Computation, CEC 2012

Biogeography-based informative gene selection and cancer classification using SVM and Random Forests

Antimicrobial peptides (AMPs) are gaining popularity as anti-infective agents. Information on sequence features that contribute to target specificity of AMPs will aid in accelerating drug discovery programs involving them. In this study, an algorithm called ClassAMP using Random Forests (RFs) and Support Vector Machines (SVMs) has been developed to predict the propensity of a protein sequence to have antibacterial, antifungal, or antiviral activity. ClassAMP is available at http://www.bicnirrh.res.in/classamp/. © 2004-2012 IEEE.

IEEE/ACM Transactions on Computational Biology and Bioinformatics

ClassAMP: A prediction tool for classification of antimicrobial peptides

Atomic structures and physical properties of Gd-doped alumina clusters-namely, GdAl 2n-1O 3n and Gd 2Al 2n-2O 3n with n = 1-10-have been studied within the framework of spin-polarized density functional theory and the projector augmented wave pseudopotential method. We find that the atomic structures of the host clusters (Al 2O 3) n are not changed significantly by the substitutional doping of a Gd atom on Al sites. Our results show that in the size range of the clusters we studied, a Gd atom prefers a maximum 4-fold-coordinated Al-site, except for n = 8, in which a 5-fold-coordinated Al site is favored. The substitution of Al with Gd atom is energetically favorable. This is in contrast to the substitutional doping of Gd in the bulk alumina corundum structure that is endothermic. There is a net magnetic moment of 7 $\mu$ B per Gd atom, which is mostly localized on the Gd atom. Further substitution of an Al atom with Gd in GdAl 2n-1O 3n clusters results in the lowest-energy configuration's being either ferromagnetic or antiferromagnetic, the difference between the two magnetic states being very small. The variation in the magnetic state is found to be associated with the variation in the coordination number of Gd atoms and the sites of the two Gd atoms. Our results suggest that Gd doping of nanoparticles offers an interesting way to prepare Gd-doped ceramic materials. {\textcopyright} 2012 American Chemical Society.

Journal of Physical Chemistry C

First principles calculations for structural, electronic and magnetic properties of gadolinium-doped alumina clusters

First principles calculations using pseudopotentials and generalized gradient approximation (GGA) for the exchange-correlation energy show that addition of Si makes Eu doping in GaN energetically favorable. It breaks local symmetry around Eu ions and leads to shallow states below the conduction band that could facilitate intra-4f shell transitions. Silicon atoms on Ga sites act as intrinsic donors transforming Eu from a 3+ to a 2+ state. The half-filled 4f states with a 7 $\mu$ B magnetic moment on each Eu ion lie within the band gap of GaN and are narrower compared with the only-Eu doping case due to reduced hybridization with the host states. There is a tendency for clustering of Eu ions with ferromagnetic coupling and the ∼5-{\AA} interatomic distance, but EuN phase formation is unfavorable. Further effects of the inclusion of onsite Coulomb interaction U within GGA+U formalism on the electronic structure are discussed. {\textcopyright} 2012 American Physical Society.

Physical Review B - Condensed Matter and Materials Physics

Energetics and electronic structure of GaN codoped with Eu and Si

Bacterial lipoproteins play critical roles in various physiological processes including the maintenance of pathogenicity and numbers of them are being considered as potential candidates for generating novel vaccines. In this work, we put forth an algorithm to identify and predict ligand-binding sites in bacterial lipoproteins. The method uses three types of pocket descriptors, namely fpocket descriptors, 3D Zernike descriptors and shell descriptors, and combines them with Support Vector Machine (SVM) method for the classification. The three types of descriptors represent shape-based properties of the pocket as well as its local physio-chemical features. All three types of descriptors, along with their hybrid combinations are evaluated with SVM and to improve classification performance, WEKA-InfoGain feature selection is applied. Results obtained in the study show that the classifier successfully differentiates between ligand-binding and non-binding pockets. For the combination of three types of descriptors, 10 fold cross-validation accuracy of 86.83% is obtained for training while the selected model achieved test Matthews Correlation Coefficient (MCC) of 0.534. Individually or in combination with new and existing methods, our model can be a very useful tool for the prediction of potential ligand-binding sites in bacterial lipoproteins. © 2012 Bentham Science Publishers.

Protein and Peptide Letters

SVM prediction of ligand-binding sites in bacterial lipoproteins employing shape and physio-chemical descriptors

In recent years, many studies have been devoted to understand the atomic and electronic structure of TiO2 because it has wide applications, for example, (1) as an alternative to indium tin oxide in the field of transparent conductors, (2) in dye-sensitized solar cells, (3) photocatalysis, (4) water splitter, (5) in paints and (6) support in heterogeneous catalysis in automobile industry for pollution control (Czekaj et al. 2009; Diebold 2003; Feng et al. 2008; Henrich and Cox 1994; Kaden et al. 2009, 2010; Menzies et al. 2004; Sellidj and Koel 1993; Zeng et al. 2008). In the latter application, noble or near noble metal nanoclusters supported on a TiO2 surface are used to oxidize (reduce) hazardous CO (NO) gas that is present in the exhaust of an automobile. In this process the role of metal clusters as well as support are crucial. The combined system, first, oxidizes CO gas where an additional oxygen atom is generally obtained from either the process of NO reduction or the environment. Second, the system is capable of releasing the oxidized CO gas into the environment from the active sites of catalyst to provide further oxidation of CO to continue the heterogeneous catalyst. If the CO2 is not released in such a catalyst, poisoning can occur (Wang et al. 2011). Much attention has been focused on nanoclusters of platinum, palladium and gold and their alloys as suitable candidates for supported clusters for such reactions. In an actual catalyst there is a distribution of cluster sizes, but it is believed that among these different-sized clusters, small clusters may be catalytically more active. Therefore, understanding the interaction of small clusters of such elements and their alloys as well as their interaction with CO molecules has attracted much interest.