Derivation and Experimental Performance of Standard and Novel Uncertainty Calibration Techniques.

To aid in the transparency of state-of-the-art machine learning models, there has been considerable research performed in uncertainty quantification (UQ). UQ aims to quantify what a model does not know by measuring variation of the model under stochastic conditions and has been demonstrated to be a potentially powerful tool for medical AI. Evaluation of UQ, however, is largely constrained to visual analysis.

A QUEST for Model Assessment: Identifying Difficult Subgroups via Epistemic Uncertainty Quantification.

Uncertainty quantification in machine learning can provide powerful insight into a model's capabilities and enhance human trust in opaque models. Well-calibrated uncertainty quantification reveals a connection between high uncertainty and an increased likelihood of an incorrect classification. We hypothesize that if we are able to explain the model's uncertainty by generating rules that define subgroups of data with high and low levels of classification uncertainty, then those same rules will identify subgroups of data on which the model performs well and subgroups on which the model does not perform well.

Artificial Intelligence Pipeline to Bridge the Gap between Bench Researchers and Clinical Researchers in Precision Medicine.

Precision medicine informatics is a field of research that incorporates learning systems that generate new knowledge to improve individualized treatments using integrated data sets and models. Given the ever-increasing volumes of data that are relevant to patient care, artificial intelligence (AI) pipelines need to be a central component of such research to speed discovery. Applying AI methodology to complex multidisciplinary information retrieval can support efforts to discover bridging concepts within collaborating communities.

Consistent trade-offs in fungal trait expression across broad spatial scales.

Fungi are the primary agents of terrestrial decomposition, yet our understanding of fungal biogeography lags far behind that of plants, animals and bacteria. Here, we use a trait-based approach to quantify the niches of 23 species of basidiomycete wood decay fungi from across North America, and explore the linkages among functional trait expression, climate and phylogeny. Our analysis reveals a fundamental trade-off between abiotic stress tolerance and competitive ability, whereby fungi with wide thermal and moisture niches exhibit lower displacement ability.

A salamander's top down effect on fungal communities in a detritivore ecosystem.

The soil decomposer community is a primary driver of carbon cycling in forest ecosystems. Understanding the processes that structure this community is critical to our understanding of the global carbon cycle. In North American forests, soil fungal communities are regulated by grazing soil invertebrates, which are in turn controlled by the predatory red-backed salamander (Plethodon cinereus).