99 citations · 223 across the 13 of their papers we have counts for
13 papers
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…
A Simple Text Mining Approach for Ranking Pairwise Associations in Biomedical Applications
Finn Kuusisto, John Steill, Zhaobin Kuang +3
We present a simple text mining method that is easy to implement, requires minimal data collection and preparation, and is easy to use for proposing ranked associations between a l…
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…