Ciência habilitada por dados de espécimes

Richard-Bollans, A., C. Aitken, A. Antonelli, C. Bitencourt, D. Goyder, E. Lucas, I. Ondo, et al. 2023. Machine learning enhances prediction of plants as potential sources of antimalarials. Frontiers in Plant Science 14. https://doi.org/10.3389/fpls.2023.1173328

Plants are a rich source of bioactive compounds and a number of plant-derived antiplasmodial compounds have been developed into pharmaceutical drugs for the prevention and treatment of malaria, a major public health challenge. However, identifying plants with antiplasmodial potential can be time-consuming and costly. One approach for selecting plants to investigate is based on ethnobotanical knowledge which, though having provided some major successes, is restricted to a relatively small group of plant species. Machine learning, incorporating ethnobotanical and plant trait data, provides a promising approach to improve the identification of antiplasmodial plants and accelerate the search for new plant-derived antiplasmodial compounds. In this paper we present a novel dataset on antiplasmodial activity for three flowering plant families – Apocynaceae, Loganiaceae and Rubiaceae (together comprising c. 21,100 species) – and demonstrate the ability of machine learning algorithms to predict the antiplasmodial potential of plant species. We evaluate the predictive capability of a variety of algorithms – Support Vector Machines, Logistic Regression, Gradient Boosted Trees and Bayesian Neural Networks – and compare these to two ethnobotanical selection approaches – based on usage as an antimalarial and general usage as a medicine. We evaluate the approaches using the given data and when the given samples are reweighted to correct for sampling biases. In both evaluation settings each of the machine learning models have a higher precision than the ethnobotanical approaches. In the bias-corrected scenario, the Support Vector classifier performs best – attaining a mean precision of 0.67 compared to the best performing ethnobotanical approach with a mean precision of 0.46. We also use the bias correction method and the Support Vector classifier to estimate the potential of plants to provide novel antiplasmodial compounds. We estimate that 7677 species in Apocynaceae, Loganiaceae and Rubiaceae warrant further investigation and that at least 1300 active antiplasmodial species are highly unlikely to be investigated by conventional approaches. While traditional and Indigenous knowledge remains vital to our understanding of people-plant relationships and an invaluable source of information, these results indicate a vast and relatively untapped source in the search for new plant-derived antiplasmodial compounds.

Xue, T., S. R. Gadagkar, T. P. Albright, X. Yang, J. Li, C. Xia, J. Wu, and S. Yu. 2021. Prioritizing conservation of biodiversity in an alpine region: Distribution pattern and conservation status of seed plants in the Qinghai-Tibetan Plateau. Global Ecology and Conservation 32: e01885. https://doi.org/10.1016/j.gecco.2021.e01885

The Qinghai-Tibetan Plateau (QTP) harbors abundant and diverse plant life owing to its high habitat heterogeneity. However, the distribution pattern of biodiversity hotspots and their conservation status remain unclear. Based on 148,283 high-resolution occurrence coordinates of 13,450 seed plants, w…

Crespo-Mendes, N., A. Laurent, and M. Z. Hauschild. 2018. Effect factors of terrestrial acidification in Brazil for use in Life Cycle Impact Assessment. The International Journal of Life Cycle Assessment 24: 1105–1117. https://doi.org/10.1007/s11367-018-1560-7

Purpose:In Life Cycle Impact Assessment, atmospheric fate factors, soil exposure factors, and effect factors are combined to characterize potential impacts of acidifying substances in terrestrial environments. Due to the low availability of global data sets, effect factors (EFs) have been reported a…

Crespo-Mendes, N., A. Laurent, H. H. Bruun, and M. Z. Hauschild. 2019. Relationships between plant species richness and soil pH at the level of biome and ecoregion in Brazil. Ecological Indicators 98: 266–275. https://doi.org/10.1016/j.ecolind.2018.11.004

Soil pH has been used to indicate how changes in soil acidity can influence species loss. The correlation between soil pH and plant species richness has mainly been studied in North America and Europe, while there is a lack of studies exploring Tropical floras. Here, our aim was therefore to investi…