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

Quantum Sensor Miniaturization

The classical bound on image resolution defined by the Rayleigh limit can be beaten by exploiting the properties of quantum mechanical entanglement. If entangled photons are used as signal states, the best possible resolution is instead given by the Heisenberg limit, an improvement proportional to the number of entangled photons in the signal. In this paper we present a novel application of entanglement by showing that the resolution obtained by an imaging system utilizing separable photons can be achieved by an imaging system making use of entangled photons, but with the advantage of a smaller aperture, thus resulting in a smaller and lighter system. This can be especially valuable in satellite imaging where weight and size play a vital role.Comment: 3 pages, 1 figure. Accepted for publication in Photonics Technology Letter

An environmental assessment of the John Pennekamp Coral Reef State Park and the Key Largo Coral Reef Marine Sanctuary (Unpublished 1983 Report)

The Pennekamp Coral Reef State Park was established in 1960 and the Key Largo National Marine Sanctuary in 1975. Field studies, funded by NOAA, were conducted in 1980 - 1981 to determine the state of the coral reefs and surrounding areas in relation to changing environmental conditions and resource management that had occurred over the intervening years. Ten reef sites within the Sanctuary and seven shallow grass and hardbottom sites within the Park were chosen for qualitative and quantitative studies. At each site, three parallel transects not less than 400 m long were run perpendicular to the reef or shore, each 300 m apart. Observations, data collecting and sampling were done by two teams of divers. Approximately 75 percent of the bottom within the 18-m isobath was covered by marine grasses, predominantly turtle grass. The general health of the seagrasses appeared good but a few areas showed signs of stress. The inner hardbottom of the Park was studied at the two entrances to Largo Sound. Though at the time of the study the North Channel hardbottom was subjected to only moderate boat traffic, marked changes had taken place over the past years, the most obvious of which was the loss of the extensive beds of Sargassum weed, one of the most extensive beds of this alga in the Keys. Only at this site was the green alga Enteromorpha encountered. This alga, often considered a pollution indicator, may denote the effects of shore run off. The hardbottom at South Channel and the surrounding grass beds showed signs of stress. This area bears the heaviest boat traffic within the Park waters causing continuous turbidity from boat wakes with resulting siltation. The offshore hardbottom and rubble areas in the Sanctuary appeared to be in good health and showed no visible indications of deterioration. Damage by boat groundings and anchors was negligible in the areas surveyed. The outer reefs in general appear to be healthy. Corals have a surprising resiliency to detrimental factors and, when conditions again become favorable, recover quickly from even severe damage. It is, therefore, a cause for concern that Grecian Rocks, which sits somewhat inshore of the outer reef line, has yet to recover from die-off in 1978. The slow recovery, if occurring, may be due to the lower quality of the inshore waters. The patch reefs, more adapted to inshore waters, do not show obvious stress signs, at least those surveyed in this study. It is apparent that water quality was changing in the keys. Water clarity over much of the reef tract was observed to be much reduced from former years and undoubtedly plays an important part in the stresses seen today over the Sanctuary and Park. (PDF contains 119 pages