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

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

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

Implementation of Design Changes Towards a More Reliable, Hands-off Magnetron Ion Source

As the main $H^{-}$ 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 $H^{-}$ 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 $H_{2}$ 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

Improvements on the Stability and Operation of a Magnetron H- Ion Source

The magnetron H- ion sources developed in the 1970s currently in operation at Fermilab provide beam to the rest of the accelerator complex. A series of modifications to these sources have been tested in a dedicated offline test stand with the aim of improving different operational issues. The solenoid type gas valve was tested as an alternative to the piezoelectric gas valve in order to avoid its temperature dependence. A new cesium oven was designed and tested in order to avoid glass pieces that were present with the previous oven, improve thermal insulation and fine tune its temperature. A current-regulated arc modulator was developed to run the ion source at a constant arc current, providing very stable beam outputs during operations. In order to reduce beam noise, the addition of small amounts of N2 gas was explored, as well as testing different cathode shapes with increasing plasma volume. This paper summarizes the studies and modifications done in the source over the last three years with the aim of improving its stability, reliability and overall performance.Comment: 8 pages, 19 figure

Observations of HONO by laser-induced fluorescence at the South Pole during ANTCI 2003

Observations of nitrous acid (HONO) by laser-induced fluorescence (LIF) at the South Pole taken during the Antarctic Troposphere Chemistry Investigation (ANTCI), which took place over the time period of Nov. 15, 2003 to Jan. 4, 2004, are presented here. The median observed mixing ratio of HONO 10 m above the snow was 5.8 pptv (mean value 6.3 pptv) with a maximum of 18.2 pptv on Nov 30th, Dec 1st, 3rd, 15th, 17th, 21st, 22nd, 25th, 27th and 28th. The measurement uncertainty is ±35%. The LIF HONO observations are compared to concurrent HONO observations performed by mist chamber/ion chromatography (MC/IC). The HONO levels reported by MC/IC are about 7.2 ± 2.3 times higher than those reported by LIF. Citation: Liao, W., A. T. Case, J. Mastromarino, D. Tan, and J. E. Dibb (2006), Observations of HONO by laser-induced fluorescence at the South Pole during ANTCI 2003, Geophys. Res. Lett., 33, L09810, doi:10.1029/2005GL025470

Radio Images of 3C 58: Expansion and Motion of its Wisp

New 1.4 GHz VLA observations of the pulsar-powered supernova remnant 3C 58 have resulted in the highest-quality radio images of this object to date. The images show filamentary structure over the body of the nebula. The present observations were combined with earlier ones from 1984 and 1991 to investigate the variability of the radio emission on a variety of time-scales. No significant changes are seen over a 110 day interval. In particular, the upper limit on the apparent projected velocity of the wisp is 0.05c. The expansion rate of the radio nebula was determined between 1984 and 2004, and is 0.014+/-0.003%/year, corresponding to a velocity of 630+/-70 km/s along the major axis. If 3C 58 is the remnant of SN 1181, it must have been strongly decelerated, which is unlikely given the absence of emission from the supernova shell. Alternatively, the low expansion speed and a number of other arguments suggest that 3C 58 may be several thousand years old and not be the remnant of SN 1181.Comment: 12 pages; accepted for publication in the Astrophysical Journa