A probabilistic approach for collective similarity-based drug–drug interaction prediction

A probabilistic approach for collective similarity-based drug-drug interaction prediction

MOTIVATION As concurrent use of multiple medications becomes ubiquitous among patients, it is crucial to characterize both adverse and synergistic interactions between drugs. Statistical methods for prediction of putative drug-drug interactions (DDIs) can guide in vitro testing and cut down significant cost and effort. With the abundance of experimental data characterizing drugs and their assoc...

A Similarity-Based Approach to Prediction

Assume we are asked to predict a real-valued variable yt based on certain characteristics xt = (xt , ..., x m t ), and on a database consisting of (xi , ..., x m i , yi) for i = 1, ..., n. Analogical reasoning suggests to combine past observations of x and y with the current values of x to generate an assessment of y is similarity-weighted averaging. Specifically, the predicted value of y, ys t...

A Similarity-based Prognostics Approach for Remaining Useful Life Prediction

Physics-based and data-driven models are the two major prognostic approaches in the literature with their own advantages and disadvantages. This paper presents a similarity-based data-driven prognostic methodology and efficiency analysis study on remaining useful life estimation results. A similarity-based prognostic model is modified to employ the most similar training samples for RUL estimati...

Similarity based learning method for drug target interaction prediction

Disambiguating Energy Disaggregation: A Collective Probabilistic Approach

Reducing household energy usage is a priority for improving the resiliency and stability of the power grid and decreasing the negative impact of energy consumption on the environment and public health. Relevant and timely feedback about the power consumption of specific appliances can help household residents reduce their energy demand. Given only a total energy reading, such as that collected ...