Integrative machine learning approach for multi-class SCOP protein fold classification

Classification and prediction of protein structure has been a central research theme in structural bioinformatics. Due to the imbalanced distribution of proteins over multi SCOP classification, most discriminative machine learning suffers the well-known ‘False Positives ’ problem when learning over these types of problems. We have devised eKISS, an ensemble machine learning specifically designed to increase the coverage of positive examples when learning under multiclass imbalanced data sets. We have applied eKISS to classify 25 SCOP folds and show that our learning system improved over classical learning methods

Multi-class protein fold classification using a new ensemble machine learning approach.

Protein structure classification represents an important process in understanding the associations between sequence and structure as well as possible functional and evolutionary relationships. Recent structural genomics initiatives and other high-throughput experiments have populated the biological databases at a rapid pace. The amount of structural data has made traditional methods such as manual inspection of the protein structure become impossible. Machine learning has been widely applied to bioinformatics and has gained a lot of success in this research area. This work proposes a novel ensemble machine learning method that improves the coverage of the classifiers under the multi-class imbalanced sample sets by integrating knowledge induced from different base classifiers, and we illustrate this idea in classifying multi-class SCOP protein fold data. We have compared our approach with PART and show that our method improves the sensitivity of the classifier in protein fold classification. Furthermore, we have extended this method to learning over multiple data types, preserving the independence of their corresponding data sources, and show that our new approach performs at least as well as the traditional technique over a single joined data source. These experimental results are encouraging, and can be applied to other bioinformatics problems similarly characterised by multi-class imbalanced data sets held in multiple data sources

Exciton and biexciton energies in bilayer systems

We report calculations of the energies of excitons and biexcitons in ideal two-dimensional bilayer systems within the effective-mass approximation with isotropic electron and hole masses. The exciton energies are obtained by a simple numerical integration technique, while the biexciton energies are obtained from diffusion quantum Monte Carlo calculations. The exciton binding energy decays as the inverse of the separation of the layers, while the binding energy of the biexciton with respect to dissociation into two separate excitons decays exponentially

The effect of manganese oxide on the sinterability of hydroxyapatite

The sinterability of manganese oxide (MnO2) doped hydroxyapatite (HA) ranging from 0.05 to 1 wt% was investigated. Green samples were prepared and sintered in air at temperatures ranging from 1000 to 1400 °C. Sintered bodies were characterized to determine the phase stability, grain size, bulk density, hardness, fracture toughness and Young's modulus. XRD analysis revealed that the HA phase stability was not disrupted throughout the sintering regime employed. In general, samples containing less than 0.5 wt% MnO2 and when sintered at lower temperatures exhibited higher mechanical properties than the undoped HA. The study revealed that all the MnO2-doped HA achieved >99% relative density when sintered at 1100–1250 °C as compared to the undoped HA which could only attained highest value of 98.9% at 1150 °C. The addition of 0.05 wt% MnO2 was found to be most beneficial as the samples exhibited the highest hardness of 7.58 GPa and fracture toughness of 1.65 MPam1/2 as compared to 5.72 GPa and 1.22 MPam1/2 for the undoped HA when sintered at 1000 °C. Additionally, it was found that the MnO2-doped samples attained E values above 110 GPa when sintered at temperature as low as 1000 °C if compared to 1050 °C for the undoped HA

Coherent Diabatic Ion Transport and Separation in a Multi-Zone Trap Array

We investigate the motional dynamics of single and multiple ions during transport between and separation into spatially distinct locations in a multi-zone linear Paul trap. A single 9Be+ ion in a 2 MHz harmonic well located in one zone was laser-cooled to near its ground state of motion and transported 370 micrometers by moving the well to another zone. This was accomplished in 8 microseconds, corresponding to 16 periods of oscillation. Starting from a state with n=0.1 quanta, during transport the ion was excited to a displaced coherent state with n=1.6 quanta but on completion was returned close to its motional ground state with n=0.2. Similar results were achieved for the transport of two ions. We also separated chains of up to 9 ions from one potential well to two distinct potential wells. With two ions this was accomplished in 55 microseconds, with final excitations of about 2 quanta for each ion. Fast coherent transport and separation can significantly reduce the time overhead in certain architectures for scalable quantum information processing with trapped ions.Comment: 5 pages, 5 figure