A review of domain adaptation without target labels

Domain adaptation has become a prominent problem setting in machine learning and related fields. This review asks the question: how can a classifier learn from a source domain and generalize to a target domain? We present a categorization of approaches, divided into, what we refer to as, sample-based, feature-based and inference-based methods. Sample-based methods focus on weighting individual observations during training based on their importance to the target domain. Feature-based methods revolve around on mapping, projecting and representing features such that a source classifier performs well on the target domain and inference-based methods incorporate adaptation into the parameter estimation procedure, for instance through constraints on the optimization procedure. Additionally, we review a number of conditions that allow for formulating bounds on the cross-domain generalization error. Our categorization highlights recurring ideas and raises questions important to further research.Comment: 20 pages, 5 figure

Efficient representation of uncertainty in multiple sequence alignments using directed acyclic graphs

Background A standard procedure in many areas of bioinformatics is to use a single multiple sequence alignment (MSA) as the basis for various types of analysis. However, downstream results may be highly sensitive to the alignment used, and neglecting the uncertainty in the alignment can lead to significant bias in the resulting inference. In recent years, a number of approaches have been developed for probabilistic sampling of alignments, rather than simply generating a single optimum. However, this type of probabilistic information is currently not widely used in the context of downstream inference, since most existing algorithms are set up to make use of a single alignment. Results In this work we present a framework for representing a set of sampled alignments as a directed acyclic graph (DAG) whose nodes are alignment columns; each path through this DAG then represents a valid alignment. Since the probabilities of individual columns can be estimated from empirical frequencies, this approach enables sample-based estimation of posterior alignment probabilities. Moreover, due to conditional independencies between columns, the graph structure encodes a much larger set of alignments than the original set of sampled MSAs, such that the effective sample size is greatly increased. Conclusions The alignment DAG provides a natural way to represent a distribution in the space of MSAs, and allows for existing algorithms to be efficiently scaled up to operate on large sets of alignments. As an example, we show how this can be used to compute marginal probabilities for tree topologies, averaging over a very large number of MSAs. This framework can also be used to generate a statistically meaningful summary alignment; example applications show that this summary alignment is consistently more accurate than the majority of the alignment samples, leading to improvements in downstream tree inference. Implementations of the methods described in this article are available at http://statalign.github.io/WeaveAlign webcite

OspA heterogeneity of Borrelia valaisiana confirmed by phenotypic and genotypic analyses

BACKGROUND: Although European Borrelia burgdorferi sensu lato isolates have been divided into five genospecies, specific tools for the serotype characterization of only three genospecies are available. Monoclonals antibodies (mAbs) H3TS, D6 and I17.3 identify B. burgdorferi sensu stricto (ss.), B. garinii and B. afzelii respectively, but no mAbs are available to identify B. valaisiana. In the same way, specific primers exist to amplify the OspA gene of B. burgdorferi ss., B. garinii and B. afzelii. The aim of the study was to develop species-specific mAb and PCR primers for the phenotypic and genetic identification of B. valaisiana. RESULTS: This study describes a mAb that targets OspA of B. valaisiana and primers targeting the OspA gene of this species. As the monoclonal antibody A116k did not react with strains NE231, M7, M53 and Frank and no amplification was observed with strains NE231, M7 and M53, the existence of two subgroups among European B. valaisiana species was confirmed. CONCLUSIONS: The association of both monoclonal antibody A116k and primers Bval 1F and Bval 1R allows to specific identification of the B. valaisiana isolates belonging to subgroup 1