An empirical comparison of supervised machine learning techniques in bioinformatics

Research in bioinformatics is driven by the experimental data. Current biological databases are populated by vast amounts of experimental data. Machine learning has been widely applied to bioinformatics and has gained a lot of success in this research area. At present, with various learning algorithms available in the literature, researchers are facing difficulties in choosing the best method that can apply to their data. We performed an empirical study on 7 individual learning systems and 9 different combined methods on 4 different biological data sets, and provide some suggested issues to be considered when answering the following questions: (i) How does one choose which algorithm is best suitable for their data set? (ii) Are combined methods better than a single approach? (iii) How does one compare the effectiveness of a particular algorithm to the others

Characterisation of FAD-family folds using a machine learning approach

Flavin adenine dinucleotide (FAD) and its derivatives play a crucial role in biological processes. They are major organic cofactors and electron carriers in both enzymatic activities and biochemical pathways. We have analysed the relationships between sequence and structure of FAD-containing proteins using a machine learning approach. Decision trees were generated using the C4.5 algorithm as a means of automatically generating rules from biological databases (TOPS, CATH and PDB). These rules were then used as background knowledge for an ILP system to characterise the four different classes of FAD-family folds classified in Dym and Eisenberg (2001). These FAD-family folds are: glutathione reductase (GR), ferredoxin reductase (FR), p-cresol methylhydroxylase (PCMH) and pyruvate oxidase (PO). Each FADfamily was characterised by a set of rules. The “knowledge patterns” generated from this approach are a set of rules containing conserved sequence motifs, secondary structure sequence elements and folding information. Every rule was then verified using statistical evaluation on the measured significance of each rule. We show that this machine learning approach is capable of learning and discovering interesting patterns from large biological databases and can generate “knowledge patterns” that characterise the FADcontaining proteins, and at the same time classify these proteins into four different families

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

A heterotic sigma model with novel target geometry

We construct a (1,2) heterotic sigma model whose target space geometry consists of a transitive Lie algebroid with complex structure on a Kaehler manifold. We show that, under certain geometrical and topological conditions, there are two distinguished topological half--twists of the heterotic sigma model leading to A and B type half--topological models. Each of these models is characterized by the usual topological BRST operator, stemming from the heterotic (0,2) supersymmetry, and a second BRST operator anticommuting with the former, originating from the (1,0) supersymmetry. These BRST operators combined in a certain way provide each half--topological model with two inequivalent BRST structures and, correspondingly, two distinct perturbative chiral algebras and chiral rings. The latter are studied in detail and characterized geometrically in terms of Lie algebroid cohomology in the quasiclassical limit.Comment: 83 pages, no figures, 2 references adde

Tunneling spectroscopy studies of aluminum oxide tunnel barrier layers

We report scanning tunneling microscopy and ballistic electron emission microscopy studies of the electronic states of the uncovered and chemisorbed-oxygen covered surface of AlOx tunnel barrier layers. These states change when chemisorbed oxygen ions are moved into the oxide by either flood gun electron bombardment or by thermal annealing. The former, if sufficiently energetic, results in locally well defined conduction band onsets at ~1 V, while the latter results in a progressively higher local conduction band onset, exceeding 2.3 V for 500 and 600 C thermal anneals

Stray field signatures of N\'eel textured skyrmions in Ir/Fe/Co/Pt multilayer films

Skyrmions are nanoscale spin configurations with topological properties that hold great promise for spintronic devices. Here, we establish their N\'eel texture, helicity, and size in Ir/Fe/Co/Pt multilayer films by constructing a multipole expansion to model their stray field signatures and applying it to magnetic force microscopy (MFM) images. Furthermore, the demonstrated sensitivity to inhomogeneity in skyrmion properties, coupled with a unique capability to estimate the pinning force governing dynamics, portends broad applicability in the burgeoning field of topological spin textures.Comment: 6 pages, 4 figures, significantly revised and upgraded. For the updated supplementary material please contact one of the corresponding author