Classification-by-Analogy: Using Vector Representations of Implicit Relationships to Identify Plausibly Causal Drug/Side-effect Relationships. - Justin Mower; Devika Subramanian; Ning Shang; Trevor Cohen (AMIA, 2016)

Learning predictive models of drug side-effect relationships from distributed representations of literature-derived semantic predications. - Justin Mower; Devika Subramanian; Trevor Cohen (J. Am. Medical Informatics Assoc., 2018)

Predicting Adverse Drug-Drug Interactions with Neural Embedding of Semantic Predications. - Hannah A. Burkhardt; Devika Subramanian; Justin Mower; Trevor A. Cohen (AMIA, 2019)

Complementing Observational Signal with Distributed Representations for Drug Side-effect Prediction. - Justin Mower; Trevor A. Cohen; Devika Subramanian (AMIA, 2019)

aer2vec: Distributed Representations of Adverse Event Reporting System Data as a Means to Identify Drug/Side-Effect Associations. - Jake Portanova; Nathan Murray; Justin Mower; Devika Subramanian; Trevor A. Cohen (AMIA, 2019)

Augmenting aer2vec: Enriching distributed representations of adverse event report data with orthographic and lexical information. - Xiruo Ding; Justin Mower; Devika Subramanian; Trevor Cohen (J. Biomed. Informatics, 2021)

Deep Representational Clustering for Endotype Discovery. - Amelia J. Averitt; Justin Mower; Nilanjana Banerjee (AMIA, 2022)

Predicting Drug Blood-Brain Barrier Penetration with Adverse Event Report Embeddings. - YiFan Wu; Justin Mower; Xiruo Ding; Oliver Li; Devika Subramanian; Trevor Cohen (AMIA, 2022)