Over the past two decades, there has been a long-standing debate about the impact of taxon sampling on phylogenetic inference. Studies have been based on both real and simulated data sets, within actual and theoretical contexts, and using different inference methods, to study the impact of taxon sampling. In some cases, conflicting conclusions have been drawn for the same data set. The main questions explored in studies to date have been about the effects of using sparse data, adding new taxa, including more characters from genome sequences and using different (or concatenated) locus regions. These questions can be reduced to more fundamental ones about the assessment of data quality and the design guidelines of taxon sampling in phylogenetic inference experiments. This review summarizes progress to date in understanding the impact of taxon sampling on the accuracy of phylogenetic analysis.

Model organisms provide opportunities to design research experiments focused on disease-related processes (e.g., using genetically engineered populations that produce phenotypes of interest). For some diseases, there may be non-obvious model organisms that can help in the study of underlying disease factors. In this study, an approach is presented that leverages knowledge about human diseases and associated biological interactions networks to identify potential model organisms for a given disease category. The approach starts with the identification of functional and interaction patterns of diseases within genetic pathways. Next, these characteristic patterns are matched to interaction networks of candidate model organisms to identify similar subsystems that have characteristic patterns for diseases of interest. The quality of a candidate model organism is then determined by the degree to which the identified subsystems match genetic pathways from validated knowledge. The results of this study suggest that non-obvious model organisms may be identified through the proposed approach.

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Genre characterization can be achieved by a variety of methods that employ lexical, syntactic, and presentation features of text to highlight key domain differences and stylistic preferences. However, these traditional methods cannot uncover some important macro-structural features that are embedded in text. Representation of text as a word graph can enable effective frameworks for analysis and identification of key topological features that characterize genres of text. In this study, we investigated graph features such as clustering coefficients, centralization, diameter, and average path lengths for eight text genres. The findings indicated key patterns that vary from a genre to another according to the stylistic differences in text. Furthermore, evidence of subgenres was found through some graph features such as number of connected components and node heterogeneity.

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