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Background Phylogenetic trees are essential diagrams used in different sciences, such as evolutionary biology or taxonomy, and they depict the relationships between a given set of taxa sharing a common ancestor. So far, a multitude of tools have already been developed to infer phylogeny, and even more to visualize the resulting trees. However, editing generated graphical plots to obtain ready-to-publish figures is still a major issue. Most available tools do not take into consideration important aspects in nomenclature, such as the use of italics for taxon names or the superscript T that must be displayed after the strain/specimen designation to denote the type strain/specimen, at least not automatically. A gap also exists to easily highlight tree branches conserved across different phylogenies containing the same taxa. The lack of available tools to achieve these tasks is challenging for scientists, since manual formatting of phylogenetic trees is very time-consuming. Results Here, we present a tool named ‘gitana’, running in Linux/Windows/Mac operating systems with R software installed. It creates ready-to-publish trees with formatting taxon nomenclature and editing options such as rerooting, clade highlighting or collapsing, among other features. Moreover, ‘gitana’ performs node comparisons among phylogenies comprising the same taxa to identify conserved branches. Conclusions ‘gitana’ is a user-friendly tool to output high-quality and ready-to-publish phylogenetic trees for users without R-coding skills. It combines dedicated functions of popular R packages for phylogeny and graphical visualization into an easy one-line-command. The users’ manual and source code are freely available at https://github.com/cristinagalisteo/gitana .

A widespread global health concern among women is the incidence of the second most leading cause of fatality which is breast cancer. Predicting the occurrence of breast cancer based on the risk factors will pave the way to an early diagnosis and an efficient treatment in a quicker time. Although there are many predictive models developed for breast cancer in the past, most of these models are generated from highly imbalanced data. The imbalanced data is usually biased towards the majority class but in cancer diagnosis, it is crucial to diagnose the patients with cancer correctly which are oftentimes the minority class. This study attempts to apply three different class balancing techniques namely oversampling (Synthetic Minority Oversampling Technique (SMOTE)), undersampling (SpreadSubsample) and a hybrid method (SMOTE and SpreadSubsample) on the Breast Cancer Surveillance Consortium (BCSC) dataset before constructing the supervised learning methods. The algorithms employed in this study include Naïve Bayes, Bayesian Network, Random Forest and Decision Tree (C4.5). The balancing method which yields the best performance across all the four classifiers were tested using the validation data to determine the final predictive model. The performances of the classifiers were evaluated using a Receiver Operating Characteristic (ROC) curve, sensitivity, and specificity.

The automatic identification of code authors based on their programming styles—known as authorship attribution or code stylometry—has become possible in recent years thanks to improvements in machine learning-based techniques for author recognition. Once feasible at scale, code stylometry can be used for well-intended or malevolent activities, including: identifying the most expert coworker on a piece of code (if authorship information goes missing); fingerprinting open source developers to pitch them unsolicited job offers; de-anonymizing developers of illegal software to pursue them. Depending on their respective goals, stakeholders have an interest in making code stylometry either more or less effective. To inform these decisions we investigate how the accuracy of code stylometry is impacted by two common software development activities: code formatting and code minification. We perform code stylometry on Python code from the Google Code Jam dataset (59 authors) using a code2vec-based author classifier on concrete syntax tree (CST) representations of input source files. We conduct the experiment using both CSTs and ASTs (abstract syntax trees). We compare the respective classification accuracies on: (1) the original dataset, (2) the dataset formatted with Black, and (3) the dataset minified with Python Minifier. Our results show that: (1) CST-based stylometry performs better than AST-based (51.00%→68%), (2) code formatting makes a significant dent (15%) in code stylometry accuracy (68%→53%), with minification subtracting a further 3% (68%→50%). While the accuracy reduction is significant for both code formatting and minification, neither is enough to make developers non-recognizable via code stylometry.