Bartek Rajwa's Profile

Multiple Instance Learning (MIL) is proposed as a variation of
supervised learning for problems with incomplete knowledge about labels of
training examples. In supervised learning, every training instance is assigned
with a discrete or real-valued label. In comparison, in MIL the labels are only
assigned to bags of instances. In the
binary case, a bag is labeled positive if at
least one instance in that
bag is positive, and the bag is labeled negative if all the instances in it are negative. There are no labels on the individual
instances. The goal of MIL is to classify unseen bags or instances based on the
labeled bags as the training data.

rdo Russo


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brettjhilton@gmail.com
Brett Hilton


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AHarris@uams.edu
Harris, Andrea


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jens.hartwig@mdc-berlin.de
Jens Hartwig@MDC


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thomas.mccloskey@iconplc.com
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Colony morphology was found to be a reliable method of screening for different enterococcal strains in the clinical samples tested.

In this study, biochemical profiles and antibiograms were unreliable methods of screening for different strains, since examples of genotypically unrelated isolates of E. faecalis, E. faecium, and E. casseliflavus that shared the same biochemical reactions and antibiotic susceptibilities were found.

The real-valued output of scoring classifiers is turned into a binary class decision by choosing a cutoff. As no cutoff is optimal according to all possible performance criteria, cutoff choice involves a trade-off among different measures. Typically, a trade-off between a pair of criteria (e.g. sensitivity versus specificity) is visualized as a cutoff-parametrized curve in the plane spanned by the two measures. Popular examples of such trade-off visualizations include receiver operating characteristic (ROC) graphs, sensitivity/specificity curves, lift charts and precision/recall plots. Fawcett (2004) provides a general introduction into evaluating scoring classifiers with a focus on ROC graphs.

Application of pesticides (50), amendment with chitin (46), compost (99) or manure, and the introduction of genetically modified microorganisms (26, 75) have all been shown to affect soil microbial community structures.

Rhizobacteria are soil-borne bacteria which live on or around the roots of plants in an area referred to as the rhizosphere, a soil zone which extends a few millimeters beyond the roots. As organic materials exude from plant roots, they create a nutrient-rich environment for soil microbes to inhabit and provide energy for their metabolic activity. Thus, the rhizosphere provides an important ecological niche for rhizobacteria and is a dynamic setting for complex interactions among and between plant and microbes (Bais et al., 2006). While the interaction between plant hosts and microbes that colonize the rhizosphere can be detrimental, as in the case of soil-borne pathogens, many bacteria are known to be beneficial to their plant hosts. It is these interactions that provide interest and promise for future directions in agriculture. For example, natural antagonists to pathogens can be useful as biological agents for the suppression or prevention of disease in plants. Other examples include, the potential use of rhizobacteria as biofertilizers, phytostimulants, or in bioremediation ([b0017], Lugtenberg et al., 2002

B.J.J. Lugtenberg, T.F.C. China-A-Woeng and G.V. Bloemberg, Microbe-plant interactions: Principles and mechanisms, Antonie van Leeuwenhoek 81 (2002), pp. 373–383. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (36)

[b0057] and [b0094]).