Classification of protein interaction sentences via gaussian processes

The increase in the availability of protein interaction studies in textual format coupled with the demand for easier access to the key results has lead to a need for text mining solutions. In the text processing pipeline, classification is a key step for extraction of small sections of relevant text. Consequently, for the task of locating protein-protein interaction sentences, we examine the use of a classifier which has rarely been applied to text, the Gaussian processes (GPs). GPs are a non-parametric probabilistic analogue to the more popular support vector machines (SVMs). We find that GPs outperform the SVM and na\"ive Bayes classifiers on binary sentence data, whilst showing equivalent performance on abstract and multiclass sentence corpora. In addition, the lack of the margin parameter, which requires costly tuning, along with the principled multiclass extensions enabled by the probabilistic framework make GPs an appealing alternative worth of further adoption

Tropical and subtropical Asia's valued tree species under threat

Tree diversity in Asia's tropical and subtropical forests is central to nature-based solutions. Species vulnerability to multiple threats, which affects the provision of ecosystem services, is poorly understood. We conducted a region-wide, spatially explicit vulnerability assessment (including overexploitation, fire, overgrazing, habitat conversion, and climate change) of 63 socio-economically important tree species selected from national priority lists and validated by an expert network representing 20 countries. Overall, 74% of the most important areas for conservation of these trees fall outside of protected areas, with species severely threatened across 47% of their native ranges. The most imminent threats are overexploitation and habitat conversion, with populations being severely threatened in an average of 24% and 16% of their distribution areas. Optimistically, our results predict relatively limited overall climate change impacts, however, some of the study species are likely to lose more than 15% of their habitat by 2050 because of climate change. We pinpoint specific natural forest areas in Malaysia and Indonesia (Borneo) as hotspots for on-site conservation of forest genetic resources, more than 82% of which do not currently fall within designated protected areas. We also identify degraded lands in Indonesia (Sumatra) as priorities for restoration where planting or assisted natural regeneration will help maintain these species into the future, while croplands in Southern India are highlighted as potentially important agroforestry options. Our study highlights the need for regionally coordinated action for effective conservation and restoration

Bayesian online classifiers for text classification and filtering

SIGIR Forum (ACM Special Interest Group on Information Retrieval)97-104FASR

Optimizing F-measures: A tale of two approaches

Proceedings of the 29th International Conference on Machine Learning, ICML 20121289-29

Tracking body core temperature in military thermal environments: An extended Kalman filter approach

10.1109/BSN.2016.7516277BSN 2016 - 13th Annual Body Sensor Networks Conference296 - 29