36 citations · 132 across the 10 of their papers we have counts for
10 papers
Understanding Heart-Failure Patients EHR Clinical Features via SHAP Interpretation of Tree-Based Machine Learning Model Predictions
Shuyu Lu, Ruoyu Chen, Wei Wei +1
Heart failure (HF) is a major cause of mortality. Accurately monitoring HF progress and adjust therapies are critical for improving patient outcomes. An experienced cardiologist ca…
Learning Latent Causal Structures with a Redundant Input Neural Network
Jonathan D. Young, Bryan Andrews, Gregory F. Cooper +1
Most causal discovery algorithms find causal structure among a set of observed variables. Learning the causal structure among latent variables remains an important open problem, pa…
Supervised Vector Quantized Variational Autoencoder for Learning Interpretable Global Representations
Yifan Xue, Michael Ding, Xinghua Lu
Learning interpretable representations of data remains a central challenge in deep learning. When training a deep generative model, the observed data are often associated with cert…
PubMedQA: A Dataset for Biomedical Research Question Answering
Qiao Jin, Bhuwan Dhingra, Zhengping Liu +2
We introduce PubMedQA, a novel biomedical question answering (QA) dataset collected from PubMed abstracts. The task of PubMedQA is to answer research questions with yes/no/maybe (e…
Deep Contextualized Biomedical Abbreviation Expansion
Qiao Jin, Jinling Liu, Xinghua Lu
Automatic identification and expansion of ambiguous abbreviations are essential for biomedical natural language processing applications, such as information retrieval and question…
Probing Biomedical Embeddings from Language Models
Qiao Jin, Bhuwan Dhingra, William W. Cohen +1
Contextualized word embeddings derived from pre-trained language models (LMs) show significant improvements on downstream NLP tasks. Pre-training on domain-specific corpora, such a…
From genome to phenome: Predicting multiple cancer phenotypes based on somatic genomic alterations via the genomic impact transformer
Yifeng Tao, Chunhui Cai, William W. Cohen +1
Cancers are mainly caused by somatic genomic alterations (SGAs) that perturb cellular signaling systems and eventually activate oncogenic processes. Therefore, understanding the fu…
An exact algorithm for the weighed mutually exclusive maximum set cover problem
Songjian Lu, Xinghua Lu
In this paper, we introduce an exact algorithm with a time complexity of for the {\sc weighted mutually exclusive maximum set cover} problem, where is the number…
An exact algorithm with the time complexity of for the weighed mutually exclusive set cover problem
Songjian Lu, Xinghua Lu
In this paper, we will introduce an exact algorithm with a time complexity of for the {\sc weighted mutually exclusive set cover} problem, where is the number of…
From data towards knowledge: Revealing the architecture of signaling systems by unifying knowledge mining and data mining of systematic perturbation data
Songjian Lu, Bo Jin, Ashley Cowart +1
Genetic and pharmacological perturbation experiments, such as deleting a gene and monitoring gene expression responses, are powerful tools for studying cellular signal transduction…