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Article
Open AccessMachine learning optimization of candidate antibody yields highly diverse sub-nanomolar affinity antibody libraries
Therapeutic antibodies are an important and rapidly growing drug modality. However, the design and discovery of early-stage antibody therapeutics remain a time and cost-intensive endeavor. Here we present an e...
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Article
Open AccessExplaining COVID-19 outbreaks with reactive SEIRD models
COVID-19 epidemics have varied dramatically in nature across the United States, where some counties have clear peaks in infections, and others have had a multitude of unpredictable and non-distinct peaks. Our ...
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Chapter and Conference Paper
Challenges and Solutions with Alignment and Enrichment of Word Embedding Models
Word embedding models offer continuous vector representations that can capture rich semantics of word co-occurrence patterns. Although these models have improved the state-of-the-art on a number of nlp tasks, man...
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Chapter and Conference Paper
Handling Oversampling in Dynamic Networks Using Link Prediction
Oversampling is a common characteristic of data representing dynamic networks. It introduces noise into representations of dynamic networks, but there has been little work so far to compensate for it. Oversamp...
