Bacterial gene content variation during the course of evolution has been widely acknowledged and its pattern has been actively modeled in recent years. Gene truncation or gene pseudogenization also plays an important role in shaping bacterial genome content. Truncated genes could also arise from small-scale lateral gene transfer events. Unfortunately, the information of truncated genes has not ...

This paper studies theoretically and empirically a method of turning machinelearning algorithms into probabilistic predictors that automatically enjoys a property of validity (perfect calibration) and is computationally e cient. The price to pay for perfect calibration is that these probabilistic predictors produce imprecise (in practice, almost precise for large data sets) probabilities. When ...

As a pivotal task in cancer therapy, outcome prediction is the foundation for tailoring and adapting a treatment planning. In this paper, we propose to use image features extracted from PET and clinical characteristics. Considering that both information sources are imprecise or noisy, a novel prediction model based on Dempster-Shafer theory is developed. Firstly, a specific loss function with s...

Managing Large-scale Probabilistic Databases Christopher Ré Chair of the Supervisory Committee: Professor Dan Suciu Computer Science & Engineering Modern applications are driven by data, and increasingly the data driving these applications are imprecise. The set of applications that generate imprecise data is diverse: In sensor database applications, the goal is to measure some aspect of the ph...

This paper studies theoretically and empirically a method of turning machinelearning algorithms into probabilistic predictors that automatically enjoys a property of validity (perfect calibration) and is computationally efficient. The price to pay for perfect calibration is that these probabilistic predictors produce imprecise (in practice, almost precise for large data sets) probabilities. Whe...

It is valuable in engineering design to distinguish between two different types of uncertainty: inherent variability and imprecision. While variability is naturally random behavior in a physical process or property, imprecision is uncertainty that is due to a lack of knowledge or information. There are many sources of imprecision in design. Sequential decision making introduces imprecision beca...

on account of the existence of uncertainty, dea occasionally faces the situation of imprecise data, especially when a set of dmus include missing data, ordinal data, interval data, stochastic data, or fuzzy data. therefore, how to evaluate the efficiency of a set of dmus in interval environments is a problem worth studying. in this paper, we discussed the new method for evaluation and ranking i...