High-throughput sequencing technologies have made it possible to study bacteria through analyzing their genome sequences. For instance, comparative genome sequence analyses can reveal the phenomenon such as gene loss, gene gain, or gene exchange in a genome. By analyzing pathogenic bacterial genomes, we can discover that pathogenic genomic regions in many pathogenic bacteria are horizontally transferred from other bacteria, and these regions are also known as pathogenicity islands (PAIs). PAIs have some detectable properties, such as having different genomic signatures than the rest of the host genomes, and containing mobility genes so that they can be integrated into the host genome. In this review, we will discuss various pathogenicity island-associated features and current computational approaches for the identification of PAIs. Existing pathogenicity island databases and related computational resources will also be discussed, so that researchers may find it to be useful for the studies of bacterial evolution and pathogenicity mechanisms.

Data Science is a successful study that incorporates varying techniques and theories from distinct fields including Mathematics, Computer Science, Economics, Business and domain knowledge. Among all components in data science, domain knowledge is the key to create high quality data products by data scientists. Wineinformatics is a new data science application that uses wine as the domain knowledge and incorporates data science and wine related datasets, including physicochemical laboratory data and wine reviews. This paper produces a brand-new dataset that contains more than 100,000 wine reviews made available by the Computational Wine Wheel. This dataset is then used to quantitatively evaluate the consistency of the Wine Spectator and all of its major reviewers through both white-box and black-box classification algorithms. Wine Spectator reviewers receive more than 87% accuracy when evaluated with the SVM method. This result supports Wine Spectator’s prestigious standing in the wine industry.

Computer-aided segmentation and border detection in dermoscopic images is one of the core components of diagnostic procedures and therapeutic interventions for skin cancer. Automated assessment tools for dermoscopy images have become an important research field mainly because of inter- and intra-observer variations in human interpretation. In this study, we compare two approaches for automatic border detection in dermoscopy images: density based clustering (DBSCAN) and Fuzzy C-Means (FCM) clustering algorithms. In the first approach, if there exists enough density –greater than certain number of points- around a point, then either a new cluster is formed around the point or an existing cluster grows by including the point and its neighbors. In the second approach FCM clustering is used. This approach has the ability to assign one data point into more than one cluster.

Wineinformatics is a field that uses machine-learning and data-mining techniques to glean useful information from wine. In this work, attributes extracted from a large dataset of over 100,000 wine reviews are used to make predictions on two variables: quality based on a “100-point scale”, and price per 750 mL bottle. These predictions were built using support vector regression. Several evaluation metrics were used for model evaluation. In addition, these regression models were compared to classification accuracies achieved in a prior work. When regression was used for classification, the results were somewhat poor; however, this was expected since the main purpose of the regression was not to classify the wines. Therefore, this paper also compares the advantages and disadvantages of both classification and regression. Regression models can successfully predict within a few points of the correct grade of a wine. On average, the model was only 1.6 points away from the actual grade and off by about $13 per bottle of wine. To the best of our knowledge, this is the first work to use a large-scale dataset of wine reviews to perform regression predictions on grade and price.

Wine has been popular with the public for centuries; in the market, there are a variety of wines to choose from. Among all, Bordeaux, France, is considered as the most famous wine region in the world. In this paper, we try to understand Bordeaux wines made in the 21st century through Wineinformatics study. We developed and studied two datasets: the first dataset is all the Bordeaux wine from 2000 to 2016; and the second one is all wines listed in a famous collection of Bordeaux wines, 1855 Bordeaux Wine Official Classification, from 2000 to 2016. A total of 14,349 wine reviews are collected in the first dataset, and 1359 wine reviews in the second dataset. In order to understand the relation between wine quality and characteristics, Naïve Bayes classifier is applied to predict the qualities (90+/89−) of wines. Support Vector Machine (SVM) classifier is also applied as a comparison. In the first dataset, SVM classifier achieves the best accuracy of 86.97%; in the second dataset, Naïve Bayes classifier achieves the best accuracy of 84.62%. Precision, recall, and f-score are also used as our measures to describe the performance of our models. Meaningful features associate with high quality 21 century Bordeaux wines are able to be presented through this research paper.