99 citations · 201 across the 8 of their papers we have counts for
7 papers · 1 filter
Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach
Devendra Singh Dhami, Siwen Yan, Gautam Kunapuli +2
Predicting and discovering drug-drug interactions (DDIs) using machine learning has been studied extensively. However, most of the approaches have focused on text data or textual r…
Temporal Poisson Square Root Graphical Models
Sinong Geng, Zhaobin Kuang, Peggy Peissig +1
We propose temporal Poisson square root graphical models (TPSQRs), a generalization of Poisson square root graphical models (PSQRs) specifically designed for modeling longitudinal…
Stochastic Learning for Sparse Discrete Markov Random Fields with Controlled Gradient Approximation Error
Sinong Geng, Zhaobin Kuang, Jie Liu +2
We study the -regularized maximum likelihood estimator/estimation (MLE) problem for discrete Markov random fields (MRFs), where efficient and scalable learning requires both s…
Beyond Textual Data: Predicting Drug-Drug Interactions from Molecular Structure Images using Siamese Neural Networks
Devendra Singh Dhami, Siwen Yan, Gautam Kunapuli +2
Predicting and discovering drug-drug interactions (DDIs) is an important problem and has been studied extensively both from medical and machine learning point of view. Almost all o…
Privacy-Preserving Collaborative Prediction using Random Forests
Irene Giacomelli, Somesh Jha, Ross Kleiman +2
We study the problem of privacy-preserving machine learning (PPML) for ensemble methods, focusing our effort on random forests. In collaborative analysis, PPML attempts to solve th…
Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation
Kendrick Boyd, Vitor Santos Costa, Jesse Davis +1
Precision-recall (PR) curves and the areas under them are widely used to summarize machine learning results, especially for data sets exhibiting class skew. They are often used ana…