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Artificial Intelligence for Medicine and Science @ Harvard's Projects

bmi702 icon bmi702

Biomedical Artificial Intelligence

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Gene prioritization by compressive data fusion and chaining

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Collective pairwise classification for multi-way (multi-relational) data analysis

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Prioritizing network communities

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Graph convolutional neural network for multirelational link prediction

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Fast graph-regularized matrix factorization

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Network inference by fusing data from diverse distributions

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Graph meta learning via local subgraphs (NeurIPS 2020)

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General Strategy for Unlearning in Graph Neural Networks

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Defending graph neural networks against adversarial attacks (NeurIPS 2020)

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GraphXAI: Resource to support the development and evaluation of GNN explainers

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Configurable, reproducible, and scalable Python programs, via Hydra

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Evolution of protein interactomes across the tree of life

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Jumping across biomedical contexts using compressive data fusion

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Similarity search in heterogeneous knowledge graphs using meta paths.

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Network-guided matrix completion

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Nimfa: Nonnegative matrix factorization in Python

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IPython notebooks demonstrating Nimfa's functionality

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OhmNet: Representation learning in multi-layer graphs

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Disease pathways in the human interactome

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Population-scale patient safety data reveal inequalities in adverse events before and during COVID-19 pandemic

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Combinatorial prediction of therapeutic perturbations using causally-inspired neural networks

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Contextualizing protein representations using deep learning on protein networks and single-cell data

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Precision Medicine Knowledge Graph (PrimeKG)

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Domain Adaptation for Time Series Under Feature and Label Shifts

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Graph Neural Networks for Irregular Time Series

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scCIPHER: Contextual deep learning on single-cell-enriched knowledge graphs in neurological disorders

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