57,909 research outputs found
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
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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 âknowledge patternsâ
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 âknowledge patternsâ that characterise the FADcontaining
proteins, and at the same time classify these proteins into four
different families
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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 âFalse Positives â 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
Implementation of Design Changes Towards a More Reliable, Hands-off Magnetron Ion Source
As the main ion source for the accelerator complex, magnetron ion
sources have been used at Fermilab since the 1970s. At the offline test stand,
new R&D is carried out to develop and upgrade the present magnetron-type
sources of ions of up to 80 mA and 35 keV beam energy in the context of
the Proton Improvement Plan. The aim of this plan is to provide high-power
proton beams for the experiments at FNAL. In order to reduce the amount of
tuning and monitoring of these ion sources, a new electronic system consisting
of a current-regulated arc discharge modulator allow the ion source to run at a
constant arc current for improved beam output and operation. A solenoid-type
gas valve feeds gas into the source precisely and independently of
ambient temperature. This summary will cover several studies and design changes
that have been tested and will eventually be implemented on the operational
magnetron sources at Fermilab. Innovative results for this type of ion source
include cathode geometries, solenoid gas valves, current controlled arc pulser,
cesium boiler redesign, gas mixtures of hydrogen and nitrogen, and duty factor
reduction, with the aim to improve source lifetime, stability, and reducing the
amount of tuning needed. In this summary, I will highlight the advances made in
ion sources at Fermilab and will outline the directions of the continuing R&D
effort.Comment: 4 pp. arXiv admin note: substantial text overlap with
arXiv:1701.0175
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