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Found 8 papers, 3 papers with code
Machine Learning-guided Lipid Nanoparticle Design for mRNA Delivery
While RNA technologies hold immense therapeutic potential in a range of applications from vaccination to gene editing, the broad implementation of these technologies is hindered by the challenge of delivering these agents effectively.
Semi-supervised Cooperative Learning for Multiomics Data Fusion
Multiomics data fusion integrates diverse data modalities, ranging from transcriptomics to proteomics, to gain a comprehensive understanding of biological systems and enhance predictions on outcomes of interest related to disease phenotypes and treatment responses.
Cooperative learning for multiview analysis
Leveraging aligned signals and allowing flexible fitting mechanisms for different modalities, cooperative learning offers a powerful approach to multiomics data fusion.
Handling Missing Data with Graph Representation Learning
GRAPE tackles the missing data problem using a graph representation, where the observations and features are viewed as two types of nodes in a bipartite graph, and the observed feature values as edges.
NGBoost: Natural Gradient Boosting for Probabilistic Prediction
NGBoost generalizes gradient boosting to probabilistic regression by treating the parameters of the conditional distribution as targets for a multiparameter boosting algorithm.
Counterfactual Reasoning for Fair Clinical Risk Prediction
We investigate the extent to which the augmented counterfactual fairness criteria may be applied to develop fair models for prolonged inpatient length of stay and mortality with observational electronic health records data.
Learning to Summarize Radiology Findings
The Impression section of a radiology report summarizes crucial radiology findings in natural language and plays a central role in communicating these findings to physicians.
The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data
We present experiments that elucidate when multitask learning with neural nets improves performance for phenotyping using EHR data relative to neural nets trained for a single phenotype and to well-tuned logistic regression baselines.
