Functional genomics in forage and turf - present status and future prospects

The recent advances in plant genomics have greatly influenced basic research in many agriculturally important crops. Along with the availability of complete genome information from the model grass species rice and the model legume species M. trucncatula, the functional genomics activities in other crop species will accelerate the genomics studies of forage and turf. Brachypodium distachyon was recently proposed as a new model plant for forage and turf grass genomics studies. The combination of bioinformatics and genomics will enhance our understanding of the molecular functions of forage and turf species. This review focuses on recent advances and applications of functional genomics for large-scale EST projects, global gene expression analyses, proteomics, and metabolic profiling, as well as the impact of functional genomics on improvement of forage and turf crops. Key words: Functional genomics, forage and turf grasses, ESTs, microarray, proteomics, metabolomics, Medicago truncatula, legume. African Journal of Biotechnology Vol. 2 (12), pp. 521-527, December 200

Improved Deep Forest Mode for Detection of Fraudulent Online Transaction

As the rapid development of online transactions, transaction frauds have also emerged seriously. The fraud strategies are characterized by specialization, industrialization, concealment and scenes. Anti-fraud technologies face many challenges under the trend of new situations. In this paper, aiming at sample imbalance and strong concealment of online transactions, we enhance the original deep forest framework to propose a deep forest-based online transaction fraud detection model. Based on the BaggingBalance method we propose, we establish a global sample imbalance processing mechanism to deal with the problem of sample imbalance. In addition, the autoencoder model is introduced into the detection model to enhance the representation learning ability. Via the three-month real online transactions data of a China's bank, the experimental results show that, evaluating by the metric of precision and recall rate, the proposed model has a beyond 10 % improvement compared to the random forest model, and a beyond 5 % improvement compared to the original deep forest model